abstracts listlengths 2 2 | id_1 stringlengths 9 14 | id_2 stringlengths 9 14 | pair_id stringlengths 20 25 | generation_prompt stringlengths 1.27k 4.48k | joint_prompt stringlengths 687 3.9k | paper1_prompt stringlengths 212 1.94k | paper2_prompt stringlengths 212 1.94k | no_context_prompt stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
[
" The ever-growing diversity of pre-training text corpora has equipped language\nmodels with generalization capabilities across various downstream tasks.\nHowever, such diverse datasets are often too large for academic budgets; hence,\nmost research on Transformer architectures, training procedures, optimizers,\ne... | 2304.08442 | 2205.12491 | 2304.08442_2205.12491 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The ever-growing diversity of pre-training text corpora has equipped language
models with generalization capabilities across various downstream tasks.
However, such diverse datasets are often too large for academic budgets; hence,
most research on Transformer architectures, training procedures, optimizers,
etc... | Paper:
The ever-growing diversity of pre-training text corpora has equipped language
models with generalization capabilities across various downstream tasks.
However, such diverse datasets are often too large for academic budgets; hence,
most research on Transformer architectures, training procedures, optimizers,
etc. ... | Paper:
Recent relation extraction (RE) works have shown encouraging improvements by
conducting contrastive learning on silver labels generated by distant
supervision before fine-tuning on gold labels. Existing methods typically
assume all these silver labels are accurate and treat them equally; however,
distant supervi... | Here's an insight: |
[
" Large language models (LLMs) have demonstrated remarkable prowess in language\nunderstanding and generation. Advancing from foundation LLMs to\ninstructionfollowing LLMs, instruction tuning plays a vital role in aligning\nLLMs to human preferences. However, the existing LLMs are usually focused on\nEnglish, lead... | 2306.10968 | 2206.11349 | 2306.10968_2206.11349 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Large language models (LLMs) have demonstrated remarkable prowess in language
understanding and generation. Advancing from foundation LLMs to
instructionfollowing LLMs, instruction tuning plays a vital role in aligning
LLMs to human preferences. However, the existing LLMs are usually focused on
English, leadin... | Paper:
Large language models (LLMs) have demonstrated remarkable prowess in language
understanding and generation. Advancing from foundation LLMs to
instructionfollowing LLMs, instruction tuning plays a vital role in aligning
LLMs to human preferences. However, the existing LLMs are usually focused on
English, leading ... | Paper:
Recent works have shown that attaching prompts to the input is effective at
conditioning Language Models (LM) to perform specific tasks. However, prompts
are always included in the input text during inference, thus incurring
substantial computational and memory overhead. Also, there is currently no
straightforwa... | Here's an insight: |
[
" Several pre-training objectives, such as masked language modeling (MLM), have\nbeen proposed to pre-train language models (e.g. BERT) with the aim of learning\nbetter language representations. However, to the best of our knowledge, no\nprevious work so far has investigated how different pre-training objectives\n... | 2203.10415 | 2210.14389 | 2203.10415_2210.14389 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Several pre-training objectives, such as masked language modeling (MLM), have
been proposed to pre-train language models (e.g. BERT) with the aim of learning
better language representations. However, to the best of our knowledge, no
previous work so far has investigated how different pre-training objectives
af... | Paper:
Several pre-training objectives, such as masked language modeling (MLM), have
been proposed to pre-train language models (e.g. BERT) with the aim of learning
better language representations. However, to the best of our knowledge, no
previous work so far has investigated how different pre-training objectives
affe... | Paper:
Research on Korean grammatical error correction (GEC) is limited, compared to
other major languages such as English. We attribute this problematic
circumstance to the lack of a carefully designed evaluation benchmark for
Korean GEC. In this work, we collect three datasets from different sources
(Kor-Lang8, Kor-N... | Here's an insight: |
[
" Given a natural language statement, how to verify its veracity against a\nlarge-scale textual knowledge source like Wikipedia? Most existing neural\nmodels make predictions without giving clues about which part of a false claim\ngoes wrong. In this paper, we propose LOREN, an approach for interpretable fact\nver... | 2012.13577 | 2302.06426 | 2012.13577_2302.06426 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Given a natural language statement, how to verify its veracity against a
large-scale textual knowledge source like Wikipedia? Most existing neural
models make predictions without giving clues about which part of a false claim
goes wrong. In this paper, we propose LOREN, an approach for interpretable fact
verif... | Paper:
Given a natural language statement, how to verify its veracity against a
large-scale textual knowledge source like Wikipedia? Most existing neural
models make predictions without giving clues about which part of a false claim
goes wrong. In this paper, we propose LOREN, an approach for interpretable fact
verific... | Paper:
Linguistic ambiguity is and has always been one of the main challenges in
Natural Language Processing (NLP) systems. Modern Transformer architectures
like BERT, T5 or more recently InstructGPT have achieved some impressive
improvements in many NLP fields, but there is still plenty of work to do.
Motivated by the... | Here's an insight: |
[
" We propose DiffCSE, an unsupervised contrastive learning framework for\nlearning sentence embeddings. DiffCSE learns sentence embeddings that are\nsensitive to the difference between the original sentence and an edited\nsentence, where the edited sentence is obtained by stochastically masking out\nthe original s... | 2204.10298 | 2211.05172 | 2204.10298_2211.05172 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
We propose DiffCSE, an unsupervised contrastive learning framework for
learning sentence embeddings. DiffCSE learns sentence embeddings that are
sensitive to the difference between the original sentence and an edited
sentence, where the edited sentence is obtained by stochastically masking out
the original sen... | Paper:
We propose DiffCSE, an unsupervised contrastive learning framework for
learning sentence embeddings. DiffCSE learns sentence embeddings that are
sensitive to the difference between the original sentence and an edited
sentence, where the edited sentence is obtained by stochastically masking out
the original sente... | Paper:
Self-supervised learning (SSL) methods such as WavLM have shown promising
speech separation (SS) results in small-scale simulation-based experiments. In
this work, we extend the exploration of the SSL-based SS by massively scaling
up both the pre-training data (more than 300K hours) and fine-tuning data (10K
hou... | Here's an insight: |
[
" When humans design cost or goal specifications for robots, they often produce\nspecifications that are ambiguous, underspecified, or beyond planners' ability\nto solve. In these cases, corrections provide a valuable tool for\nhuman-in-the-loop robot control. Corrections might take the form of new goal\nspecifica... | 2204.05186 | 2303.10583 | 2204.05186_2303.10583 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
When humans design cost or goal specifications for robots, they often produce
specifications that are ambiguous, underspecified, or beyond planners' ability
to solve. In these cases, corrections provide a valuable tool for
human-in-the-loop robot control. Corrections might take the form of new goal
specificati... | Paper:
When humans design cost or goal specifications for robots, they often produce
specifications that are ambiguous, underspecified, or beyond planners' ability
to solve. In these cases, corrections provide a valuable tool for
human-in-the-loop robot control. Corrections might take the form of new goal
specification... | Paper:
In the early stages of the design process, designers explore opportunities by
discovering unmet needs and developing innovative concepts as potential
solutions. From a human-centered design perspective, designers must develop
empathy with people to truly understand their needs. However, developing
empathy is a c... | Here's an insight: |
[
" Pre-training methods with contrastive learning objectives have shown\nremarkable success in dialog understanding tasks. However, current contrastive\nlearning solely considers the self-augmented dialog samples as positive samples\nand treats all other dialog samples as negative ones, which enforces dissimilar\nr... | 2209.06638 | 2205.15868 | 2209.06638_2205.15868 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Pre-training methods with contrastive learning objectives have shown
remarkable success in dialog understanding tasks. However, current contrastive
learning solely considers the self-augmented dialog samples as positive samples
and treats all other dialog samples as negative ones, which enforces dissimilar
rep... | Paper:
Pre-training methods with contrastive learning objectives have shown
remarkable success in dialog understanding tasks. However, current contrastive
learning solely considers the self-augmented dialog samples as positive samples
and treats all other dialog samples as negative ones, which enforces dissimilar
repre... | Paper:
Large-scale pretrained transformers have created milestones in text (GPT-3)
and text-to-image (DALL-E and CogView) generation. Its application to video
generation is still facing many challenges: The potential huge computation cost
makes the training from scratch unaffordable; The scarcity and weak relevance
of ... | Here's an insight: |
[
" Recent progress in language model pre-training has achieved a great success\nvia leveraging large-scale unstructured textual data. However, it is still a\nchallenge to apply pre-training on structured tabular data due to the absence\nof large-scale high-quality tabular data. In this paper, we propose TAPEX to\ns... | 2107.07653 | 2204.12679 | 2107.07653_2204.12679 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Recent progress in language model pre-training has achieved a great success
via leveraging large-scale unstructured textual data. However, it is still a
challenge to apply pre-training on structured tabular data due to the absence
of large-scale high-quality tabular data. In this paper, we propose TAPEX to
sho... | Paper:
Recent progress in language model pre-training has achieved a great success
via leveraging large-scale unstructured textual data. However, it is still a
challenge to apply pre-training on structured tabular data due to the absence
of large-scale high-quality tabular data. In this paper, we propose TAPEX to
show ... | Paper:
Document-level relation extraction (DocRE) aims to determine the relation
between two entities from a document of multiple sentences. Recent studies
typically represent the entire document by sequence- or graph-based models to
predict the relations of all entity pairs. However, we find that such a model
is not r... | Here's an insight: |
[
" Recent advances in NLP are brought by a range of large-scale pretrained\nlanguage models (PLMs). These PLMs have brought significant performance gains\nfor a range of NLP tasks, circumventing the need to customize complex designs\nfor specific tasks. However, most current work focus on finetuning PLMs on a\ndoma... | 2211.03154 | 2204.06518 | 2211.03154_2204.06518 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Recent advances in NLP are brought by a range of large-scale pretrained
language models (PLMs). These PLMs have brought significant performance gains
for a range of NLP tasks, circumventing the need to customize complex designs
for specific tasks. However, most current work focus on finetuning PLMs on a
domain... | Paper:
Recent advances in NLP are brought by a range of large-scale pretrained
language models (PLMs). These PLMs have brought significant performance gains
for a range of NLP tasks, circumventing the need to customize complex designs
for specific tasks. However, most current work focus on finetuning PLMs on a
domain-s... | Paper:
Text-based communication is highly favoured as a communication method,
especially in business environments. As a result, it is often abused by sending
malicious messages, e.g., spam emails, to deceive users into relaying personal
information, including online accounts credentials or banking details. For this
rea... | Here's an insight: |
[
" Adapter-tuning is a paradigm that transfers a pretrained language model to\ndownstream tasks by adding and tuning a small number of new parameters.\nPreviously proposed adapter architectures are all feed-forward neural networks.\nIn this paper, we investigate the effectiveness of using tiny-attention --\ni.e., a... | 2211.01979 | 2306.04050 | 2211.01979_2306.04050 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Adapter-tuning is a paradigm that transfers a pretrained language model to
downstream tasks by adding and tuning a small number of new parameters.
Previously proposed adapter architectures are all feed-forward neural networks.
In this paper, we investigate the effectiveness of using tiny-attention --
i.e., att... | Paper:
Adapter-tuning is a paradigm that transfers a pretrained language model to
downstream tasks by adding and tuning a small number of new parameters.
Previously proposed adapter architectures are all feed-forward neural networks.
In this paper, we investigate the effectiveness of using tiny-attention --
i.e., atten... | Paper:
We provide new estimates of an asymptotic upper bound on the entropy of
English using the large language model LLaMA-7B as a predictor for the next
token given a window of past tokens. This estimate is significantly smaller
than currently available estimates in \cite{cover1978convergent},
\cite{lutati2023focus}.... | Here's an insight: |
[
" The paper describes the open Russian medical language understanding benchmark\ncovering several task types (classification, question answering, natural\nlanguage inference, named entity recognition) on a number of novel text sets.\nGiven the sensitive nature of the data in healthcare, such a benchmark\npartially... | 2201.06499 | 2201.01209 | 2201.06499_2201.01209 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The paper describes the open Russian medical language understanding benchmark
covering several task types (classification, question answering, natural
language inference, named entity recognition) on a number of novel text sets.
Given the sensitive nature of the data in healthcare, such a benchmark
partially c... | Paper:
The paper describes the open Russian medical language understanding benchmark
covering several task types (classification, question answering, natural
language inference, named entity recognition) on a number of novel text sets.
Given the sensitive nature of the data in healthcare, such a benchmark
partially clo... | Paper:
Speech-based inputs have been gaining significant momentum with the
popularity of smartphones and tablets in our daily lives, since voice is the
most easiest and efficient way for human-computer interaction. This paper works
towards designing more effective speech-based interfaces to query the
structured data in... | Here's an insight: |
[
" Multimodal knowledge graph completion (MKGC) aims to predict missing entities\nin MKGs. Previous works usually share relation representation across\nmodalities. This results in mutual interference between modalities during\ntraining, since for a pair of entities, the relation from one modality probably\ncontradi... | 2210.08821 | 2305.19709 | 2210.08821_2305.19709 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Multimodal knowledge graph completion (MKGC) aims to predict missing entities
in MKGs. Previous works usually share relation representation across
modalities. This results in mutual interference between modalities during
training, since for a pair of entities, the relation from one modality probably
contradict... | Paper:
Multimodal knowledge graph completion (MKGC) aims to predict missing entities
in MKGs. Previous works usually share relation representation across
modalities. This results in mutual interference between modalities during
training, since for a pair of entities, the relation from one modality probably
contradicts ... | Paper:
We present XPhoneBERT, the first multilingual model pre-trained to learn
phoneme representations for the downstream text-to-speech (TTS) task. Our
XPhoneBERT has the same model architecture as BERT-base, trained using the
RoBERTa pre-training approach on 330M phoneme-level sentences from nearly 100
languages and... | Here's an insight: |
[
" We study learning from user feedback for extractive question answering by\nsimulating feedback using supervised data. We cast the problem as contextual\nbandit learning, and analyze the characteristics of several learning scenarios\nwith focus on reducing data annotation. We show that systems initially trained\n... | 2203.10079 | 2006.01245 | 2203.10079_2006.01245 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
We study learning from user feedback for extractive question answering by
simulating feedback using supervised data. We cast the problem as contextual
bandit learning, and analyze the characteristics of several learning scenarios
with focus on reducing data annotation. We show that systems initially trained
on... | Paper:
We study learning from user feedback for extractive question answering by
simulating feedback using supervised data. We cast the problem as contextual
bandit learning, and analyze the characteristics of several learning scenarios
with focus on reducing data annotation. We show that systems initially trained
on a... | Paper:
In Ordinal Classification tasks, items have to be assigned to classes that
have a relative ordering, such as positive, neutral, negative in sentiment
analysis. Remarkably, the most popular evaluation metrics for ordinal
classification tasks either ignore relevant information (for instance,
precision/recall on ea... | Here's an insight: |
[
" Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently\nused in software engineering as models pre-trained on large source code\ncorpora. Their knowledge is transferred to downstream tasks (e.g. code clone\ndetection) via fine-tuning. In natural language processing (NLP), other\nalternatives f... | 2204.08653 | 2305.02156 | 2204.08653_2305.02156 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently
used in software engineering as models pre-trained on large source code
corpora. Their knowledge is transferred to downstream tasks (e.g. code clone
detection) via fine-tuning. In natural language processing (NLP), other
alternatives for... | Paper:
Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently
used in software engineering as models pre-trained on large source code
corpora. Their knowledge is transferred to downstream tasks (e.g. code clone
detection) via fine-tuning. In natural language processing (NLP), other
alternatives for t... | Paper:
Supervised ranking methods based on bi-encoder or cross-encoder architectures
have shown success in multi-stage text ranking tasks, but they require large
amounts of relevance judgments as training data. In this work, we propose
Listwise Reranker with a Large Language Model (LRL), which achieves strong
reranking... | Here's an insight: |
[
" Retrieval-augmented Neural Machine Translation models have been successful in\nmany translation scenarios. Different from previous works that make use of\nmutually similar but redundant translation memories~(TMs), we propose a new\nretrieval-augmented NMT to model contrastively retrieved translation memories\nth... | 2212.03140 | 2208.00748 | 2212.03140_2208.00748 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Retrieval-augmented Neural Machine Translation models have been successful in
many translation scenarios. Different from previous works that make use of
mutually similar but redundant translation memories~(TMs), we propose a new
retrieval-augmented NMT to model contrastively retrieved translation memories
that... | Paper:
Retrieval-augmented Neural Machine Translation models have been successful in
many translation scenarios. Different from previous works that make use of
mutually similar but redundant translation memories~(TMs), we propose a new
retrieval-augmented NMT to model contrastively retrieved translation memories
that a... | Paper:
Transformer-based pretrained language models (LMs) are ubiquitous across
natural language understanding, but cannot be applied to long sequences such as
stories, scientific articles and long documents, due to their quadratic
complexity. While a myriad of efficient transformer variants have been
proposed, they ar... | Here's an insight: |
[
" Tables are often created with hierarchies, but existing works on table\nreasoning mainly focus on flat tables and neglect hierarchical tables.\nHierarchical tables challenge existing methods by hierarchical indexing, as\nwell as implicit relationships of calculation and semantics. This work presents\nHiTab, a fr... | 2108.06712 | 2204.06518 | 2108.06712_2204.06518 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Tables are often created with hierarchies, but existing works on table
reasoning mainly focus on flat tables and neglect hierarchical tables.
Hierarchical tables challenge existing methods by hierarchical indexing, as
well as implicit relationships of calculation and semantics. This work presents
HiTab, a free... | Paper:
Tables are often created with hierarchies, but existing works on table
reasoning mainly focus on flat tables and neglect hierarchical tables.
Hierarchical tables challenge existing methods by hierarchical indexing, as
well as implicit relationships of calculation and semantics. This work presents
HiTab, a free a... | Paper:
Text-based communication is highly favoured as a communication method,
especially in business environments. As a result, it is often abused by sending
malicious messages, e.g., spam emails, to deceive users into relaying personal
information, including online accounts credentials or banking details. For this
rea... | Here's an insight: |
[
" Instruction tuning is an emergent paradigm in NLP wherein natural language\ninstructions are leveraged with language models to induce zero-shot performance\non unseen tasks. Instructions have been shown to enable good performance on\nunseen tasks and datasets in both large and small language models. Dialogue is\... | 2205.12673 | 2010.12676 | 2205.12673_2010.12676 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Instruction tuning is an emergent paradigm in NLP wherein natural language
instructions are leveraged with language models to induce zero-shot performance
on unseen tasks. Instructions have been shown to enable good performance on
unseen tasks and datasets in both large and small language models. Dialogue is
a... | Paper:
Instruction tuning is an emergent paradigm in NLP wherein natural language
instructions are leveraged with language models to induce zero-shot performance
on unseen tasks. Instructions have been shown to enable good performance on
unseen tasks and datasets in both large and small language models. Dialogue is
an ... | Paper:
Abstract Meaning Representations (AMR) are a broad-coverage semantic
formalism which represents sentence meaning as a directed acyclic graph. To
train most AMR parsers, one needs to segment the graph into subgraphs and align
each such subgraph to a word in a sentence; this is normally done at
preprocessing, rely... | Here's an insight: |
[
" Contrastive learning models have achieved great success in unsupervised\nvisual representation learning, which maximize the similarities between feature\nrepresentations of different views of the same image, while minimize the\nsimilarities between feature representations of views of different images. In\ntext s... | 2109.03481 | 2106.00903 | 2109.03481_2106.00903 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Contrastive learning models have achieved great success in unsupervised
visual representation learning, which maximize the similarities between feature
representations of different views of the same image, while minimize the
similarities between feature representations of views of different images. In
text sum... | Paper:
Contrastive learning models have achieved great success in unsupervised
visual representation learning, which maximize the similarities between feature
representations of different views of the same image, while minimize the
similarities between feature representations of views of different images. In
text summa... | Paper:
Knowledge distillation (KD) is commonly used to construct synthetic data for
training non-autoregressive translation (NAT) models. However, there exists a
discrepancy on low-frequency words between the distilled and the original data,
leading to more errors on predicting low-frequency words. To alleviate the
pro... | Here's an insight: |
[
" While neural text-to-speech systems perform remarkably well in high-resource\nscenarios, they cannot be applied to the majority of the over 6,000 spoken\nlanguages in the world due to a lack of appropriate training data. In this\nwork, we use embeddings derived from articulatory vectors rather than\nembeddings d... | 2203.03191 | 2303.10368 | 2203.03191_2303.10368 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
While neural text-to-speech systems perform remarkably well in high-resource
scenarios, they cannot be applied to the majority of the over 6,000 spoken
languages in the world due to a lack of appropriate training data. In this
work, we use embeddings derived from articulatory vectors rather than
embeddings der... | Paper:
While neural text-to-speech systems perform remarkably well in high-resource
scenarios, they cannot be applied to the majority of the over 6,000 spoken
languages in the world due to a lack of appropriate training data. In this
work, we use embeddings derived from articulatory vectors rather than
embeddings deriv... | Paper:
Large-scale pre-trained language models (PLMs) such as BERT have recently
achieved great success and become a milestone in natural language processing
(NLP). It is now the consensus of the NLP community to adopt PLMs as the
backbone for downstream tasks. In recent works on knowledge graph question
answering (KGQ... | Here's an insight: |
[
" The widespread dissemination of toxic online posts is increasingly damaging\nto society. However, research on detecting toxic language in Chinese has lagged\nsignificantly. Existing datasets lack fine-grained annotation of toxic types\nand expressions, and ignore the samples with indirect toxicity. In addition, ... | 2305.04446 | 2304.11164 | 2305.04446_2304.11164 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The widespread dissemination of toxic online posts is increasingly damaging
to society. However, research on detecting toxic language in Chinese has lagged
significantly. Existing datasets lack fine-grained annotation of toxic types
and expressions, and ignore the samples with indirect toxicity. In addition, i... | Paper:
The widespread dissemination of toxic online posts is increasingly damaging
to society. However, research on detecting toxic language in Chinese has lagged
significantly. Existing datasets lack fine-grained annotation of toxic types
and expressions, and ignore the samples with indirect toxicity. In addition, it
... | Paper:
Language models have become very popular recently and many claims have been
made about their abilities, including for commonsense reasoning. Given the
increasingly better results of current language models on previous static
benchmarks for commonsense reasoning, we explore an alternative dialectical
evaluation. ... | Here's an insight: |
[
" Reliable methods for automatic readability assessment have the potential to\nimpact a variety of fields, ranging from machine translation to self-informed\nlearning. Recently, large language models for the German language (such as\nGBERT and GPT-2-Wechsel) have become available, allowing to develop Deep\nLearnin... | 2209.04299 | 2205.02014 | 2209.04299_2205.02014 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Reliable methods for automatic readability assessment have the potential to
impact a variety of fields, ranging from machine translation to self-informed
learning. Recently, large language models for the German language (such as
GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep
Learning ... | Paper:
Reliable methods for automatic readability assessment have the potential to
impact a variety of fields, ranging from machine translation to self-informed
learning. Recently, large language models for the German language (such as
GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep
Learning ba... | Paper:
Real-world natural language processing (NLP) models need to be continually
updated to fix the prediction errors in out-of-distribution (OOD) data streams
while overcoming catastrophic forgetting. However, existing continual learning
(CL) problem setups cannot cover such a realistic and complex scenario. In
respo... | Here's an insight: |
[
" Self-attention is a key enabler of state-of-art accuracy for various\ntransformer-based Natural Language Processing models. This attention mechanism\ncalculates a correlation score for each word with respect to the other words in\na sentence. Commonly, only a small subset of words highly correlates with the\nwor... | 2204.03227 | 2203.10545 | 2204.03227_2203.10545 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Self-attention is a key enabler of state-of-art accuracy for various
transformer-based Natural Language Processing models. This attention mechanism
calculates a correlation score for each word with respect to the other words in
a sentence. Commonly, only a small subset of words highly correlates with the
word ... | Paper:
Self-attention is a key enabler of state-of-art accuracy for various
transformer-based Natural Language Processing models. This attention mechanism
calculates a correlation score for each word with respect to the other words in
a sentence. Commonly, only a small subset of words highly correlates with the
word un... | Paper:
Named entity recognition (NER) is a fundamental task in natural language
processing. Recent works treat named entity recognition as a reading
comprehension task, constructing type-specific queries manually to extract
entities. This paradigm suffers from three issues. First, type-specific queries
can only extract... | Here's an insight: |
[
" Recently, Language Models (LMs) instruction-tuned on multiple tasks, also\nknown as multitask-prompted fine-tuning (MT), have shown the capability to\ngeneralize to unseen tasks. Previous work has shown that scaling the number of\ntraining tasks is the key component in making stronger MT LMs. In this work, we\nr... | 2302.03202 | 2205.02035 | 2302.03202_2205.02035 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also
known as multitask-prompted fine-tuning (MT), have shown the capability to
generalize to unseen tasks. Previous work has shown that scaling the number of
training tasks is the key component in making stronger MT LMs. In this work, we
rep... | Paper:
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also
known as multitask-prompted fine-tuning (MT), have shown the capability to
generalize to unseen tasks. Previous work has shown that scaling the number of
training tasks is the key component in making stronger MT LMs. In this work, we
repor... | Paper:
Despite the recent advances in abstractive summarization systems, it is still
difficult to determine whether a generated summary is factual consistent with
the source text. To this end, the latest approach is to train a factual
consistency classifier on factually consistent and inconsistent summaries.
Luckily, t... | Here's an insight: |
[
" For most natural language processing tasks, the dominant practice is to\nfinetune large pretrained transformer models (e.g., BERT) using smaller\ndownstream datasets. Despite the success of this approach, it remains unclear\nto what extent these gains are attributable to the massive background corpora\nemployed ... | 2209.14389 | 2305.20010 | 2209.14389_2305.20010 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
For most natural language processing tasks, the dominant practice is to
finetune large pretrained transformer models (e.g., BERT) using smaller
downstream datasets. Despite the success of this approach, it remains unclear
to what extent these gains are attributable to the massive background corpora
employed fo... | Paper:
For most natural language processing tasks, the dominant practice is to
finetune large pretrained transformer models (e.g., BERT) using smaller
downstream datasets. Despite the success of this approach, it remains unclear
to what extent these gains are attributable to the massive background corpora
employed for ... | Paper:
We present "Human or Not?", an online game inspired by the Turing test, that
measures the capability of AI chatbots to mimic humans in dialog, and of humans
to tell bots from other humans. Over the course of a month, the game was played
by over 1.5 million users who engaged in anonymous two-minute chat sessions
... | Here's an insight: |
[
" Given the ubiquitous nature of numbers in text, reasoning with numbers to\nperform simple calculations is an important skill of AI systems. While many\ndatasets and models have been developed to this end, state-of-the-art AI\nsystems are brittle; failing to perform the underlying mathematical reasoning\nwhen the... | 2204.05660 | 2205.04605 | 2204.05660_2205.04605 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Given the ubiquitous nature of numbers in text, reasoning with numbers to
perform simple calculations is an important skill of AI systems. While many
datasets and models have been developed to this end, state-of-the-art AI
systems are brittle; failing to perform the underlying mathematical reasoning
when they ... | Paper:
Given the ubiquitous nature of numbers in text, reasoning with numbers to
perform simple calculations is an important skill of AI systems. While many
datasets and models have been developed to this end, state-of-the-art AI
systems are brittle; failing to perform the underlying mathematical reasoning
when they ap... | Paper:
User language data can contain highly sensitive personal content. As such, it
is imperative to offer users a strong and interpretable privacy guarantee when
learning from their data. In this work, we propose SentDP: pure local
differential privacy at the sentence level for a single user document. We
propose a no... | Here's an insight: |
[
" Semantic communication in the 6G era has been deemed a promising\ncommunication paradigm to break through the bottleneck of traditional\ncommunications. However, its applications for the multi-user scenario,\nespecially the broadcasting case, remain under-explored. To effectively exploit\nthe benefits enabled by... | 2209.09425 | 2204.04748 | 2209.09425_2204.04748 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Semantic communication in the 6G era has been deemed a promising
communication paradigm to break through the bottleneck of traditional
communications. However, its applications for the multi-user scenario,
especially the broadcasting case, remain under-explored. To effectively exploit
the benefits enabled by s... | Paper:
Semantic communication in the 6G era has been deemed a promising
communication paradigm to break through the bottleneck of traditional
communications. However, its applications for the multi-user scenario,
especially the broadcasting case, remain under-explored. To effectively exploit
the benefits enabled by sem... | Paper:
Large pretrained language models (PLMs) typically tokenize the input string
into contiguous subwords before any pretraining or inference. However, previous
studies have claimed that this form of subword tokenization is inadequate for
processing morphologically-rich languages (MRLs). We revisit this hypothesis by... | Here's an insight: |
[
" Although large conversational AI models such as OpenAI's ChatGPT have\ndemonstrated great potential, we question whether such models can guarantee\nfactual accuracy. Recently, technology companies such as Microsoft and Google\nhave announced new services which aim to combine search engines with\nconversational A... | 2304.11076 | 2306.08401 | 2304.11076_2306.08401 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Although large conversational AI models such as OpenAI's ChatGPT have
demonstrated great potential, we question whether such models can guarantee
factual accuracy. Recently, technology companies such as Microsoft and Google
have announced new services which aim to combine search engines with
conversational AI.... | Paper:
Although large conversational AI models such as OpenAI's ChatGPT have
demonstrated great potential, we question whether such models can guarantee
factual accuracy. Recently, technology companies such as Microsoft and Google
have announced new services which aim to combine search engines with
conversational AI. H... | Paper:
Open-domain dialogue systems have made promising progress in recent years.
While the state-of-the-art dialogue agents are built upon large-scale
text-based social media data and large pre-trained models, there is no
guarantee these agents could also perform well in fast-growing scenarios, such
as live streaming,... | Here's an insight: |
[
" Despite the effectiveness of utilizing the BERT model for document ranking,\nthe high computational cost of such approaches limits their uses. To this end,\nthis paper first empirically investigates the effectiveness of two knowledge\ndistillation models on the document ranking task. In addition, on top of the\n... | 2009.07531 | 2202.03829 | 2009.07531_2202.03829 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Despite the effectiveness of utilizing the BERT model for document ranking,
the high computational cost of such approaches limits their uses. To this end,
this paper first empirically investigates the effectiveness of two knowledge
distillation models on the document ranking task. In addition, on top of the
re... | Paper:
Despite the effectiveness of utilizing the BERT model for document ranking,
the high computational cost of such approaches limits their uses. To this end,
this paper first empirically investigates the effectiveness of two knowledge
distillation models on the document ranking task. In addition, on top of the
rece... | Paper:
Despite its importance, the time variable has been largely neglected in the
NLP and language model literature. In this paper, we present TimeLMs, a set of
language models specialized on diachronic Twitter data. We show that a
continual learning strategy contributes to enhancing Twitter-based language
models' cap... | Here's an insight: |
[
" The growing size of neural language models has led to increased attention in\nmodel compression. The two predominant approaches are pruning, which gradually\nremoves weights from a pre-trained model, and distillation, which trains a\nsmaller compact model to match a larger one. Pruning methods can significantly\... | 2204.00408 | 2203.10012 | 2204.00408_2203.10012 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The growing size of neural language models has led to increased attention in
model compression. The two predominant approaches are pruning, which gradually
removes weights from a pre-trained model, and distillation, which trains a
smaller compact model to match a larger one. Pruning methods can significantly
r... | Paper:
The growing size of neural language models has led to increased attention in
model compression. The two predominant approaches are pruning, which gradually
removes weights from a pre-trained model, and distillation, which trains a
smaller compact model to match a larger one. Pruning methods can significantly
red... | Paper:
This is a report on the NSF Future Directions Workshop on Automatic
Evaluation of Dialog. The workshop explored the current state of the art along
with its limitations and suggested promising directions for future work in this
important and very rapidly changing area of research.
This suggests:
| Here's an insight: |
[
" With the increasing number of clinical trial reports generated every day, it\nis becoming hard to keep up with novel discoveries that inform evidence-based\nhealthcare recommendations. To help automate this process and assist medical\nexperts, NLP solutions are being developed. This motivated the SemEval-2023\nT... | 2304.13180 | 2209.10918 | 2304.13180_2209.10918 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
With the increasing number of clinical trial reports generated every day, it
is becoming hard to keep up with novel discoveries that inform evidence-based
healthcare recommendations. To help automate this process and assist medical
experts, NLP solutions are being developed. This motivated the SemEval-2023
Tas... | Paper:
With the increasing number of clinical trial reports generated every day, it
is becoming hard to keep up with novel discoveries that inform evidence-based
healthcare recommendations. To help automate this process and assist medical
experts, NLP solutions are being developed. This motivated the SemEval-2023
Task ... | Paper:
This paper tackles an emerging and challenging problem of long video temporal
grounding~(VTG) that localizes video moments related to a natural language (NL)
query. Compared with short videos, long videos are also highly demanded but
less explored, which brings new challenges in higher inference computation cost... | Here's an insight: |
[
" Automated reasoning with unstructured natural text is a key requirement for\nmany potential applications of NLP and for developing robust AI systems.\nRecently, Language Models (LMs) have demonstrated complex reasoning capacities\neven without any finetuning. However, existing evaluation for automated\nreasoning... | 2306.07934 | 2112.08804 | 2306.07934_2112.08804 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Automated reasoning with unstructured natural text is a key requirement for
many potential applications of NLP and for developing robust AI systems.
Recently, Language Models (LMs) have demonstrated complex reasoning capacities
even without any finetuning. However, existing evaluation for automated
reasoning a... | Paper:
Automated reasoning with unstructured natural text is a key requirement for
many potential applications of NLP and for developing robust AI systems.
Recently, Language Models (LMs) have demonstrated complex reasoning capacities
even without any finetuning. However, existing evaluation for automated
reasoning ass... | Paper:
We present CrossSum, a large-scale cross-lingual summarization dataset
comprising 1.68 million article-summary samples in 1,500+ language pairs. We
create CrossSum by aligning parallel articles written in different languages
via cross-lingual retrieval from a multilingual abstractive summarization
dataset and pe... | Here's an insight: |
[
" Linking computational natural language processing (NLP) models and neural\nresponses to language in the human brain on the one hand facilitates the effort\ntowards disentangling the neural representations underpinning language\nperception, on the other hand provides neurolinguistics evidence to evaluate\nand imp... | 2303.14871 | 2205.06439 | 2303.14871_2205.06439 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Linking computational natural language processing (NLP) models and neural
responses to language in the human brain on the one hand facilitates the effort
towards disentangling the neural representations underpinning language
perception, on the other hand provides neurolinguistics evidence to evaluate
and impro... | Paper:
Linking computational natural language processing (NLP) models and neural
responses to language in the human brain on the one hand facilitates the effort
towards disentangling the neural representations underpinning language
perception, on the other hand provides neurolinguistics evidence to evaluate
and improve... | Paper:
Due to the labor-intensive nature of manual test oracle construction, various
automated testing techniques have been proposed to enhance the reliability of
Natural Language Processing (NLP) software. In theory, these techniques mutate
an existing test case (e.g., a sentence with its label) and assume the
generat... | Here's an insight: |
[
" Summarization systems make numerous \"decisions\" about summary properties\nduring inference, e.g. degree of copying, specificity and length of outputs,\netc. However, these are implicitly encoded within model parameters and specific\nstyles cannot be enforced. To address this, we introduce HydraSum, a new\nsumm... | 2110.04400 | 2305.13281 | 2110.04400_2305.13281 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Summarization systems make numerous "decisions" about summary properties
during inference, e.g. degree of copying, specificity and length of outputs,
etc. However, these are implicitly encoded within model parameters and specific
styles cannot be enforced. To address this, we introduce HydraSum, a new
summariz... | Paper:
Summarization systems make numerous "decisions" about summary properties
during inference, e.g. degree of copying, specificity and length of outputs,
etc. However, these are implicitly encoded within model parameters and specific
styles cannot be enforced. To address this, we introduce HydraSum, a new
summarizat... | Paper:
A prominent weakness of modern language models (LMs) is their tendency to
generate factually incorrect text, which hinders their usability. A natural
question is whether such factual errors can be detected automatically. Inspired
by truth-seeking mechanisms in law, we propose a factuality evaluation
framework fo... | Here's an insight: |
[
" Background: Electronic Health Records hold detailed longitudinal information\nabout each patient's health status and general clinical history, a large\nportion of which is stored within the unstructured text. Existing approaches\nfocus mostly on structured data and a subset of single-domain outcomes. We\nexplore... | 2212.08072 | 2205.01703 | 2212.08072_2205.01703 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Background: Electronic Health Records hold detailed longitudinal information
about each patient's health status and general clinical history, a large
portion of which is stored within the unstructured text. Existing approaches
focus mostly on structured data and a subset of single-domain outcomes. We
explore h... | Paper:
Background: Electronic Health Records hold detailed longitudinal information
about each patient's health status and general clinical history, a large
portion of which is stored within the unstructured text. Existing approaches
focus mostly on structured data and a subset of single-domain outcomes. We
explore how... | Paper:
Self-supervised pretraining has made few-shot learning possible for many NLP
tasks. But the pretraining objectives are not typically adapted specifically
for in-context few-shot learning. In this paper, we propose to use
self-supervision in an intermediate training stage between pretraining and
downstream few-sh... | Here's an insight: |
[
" Parameter-efficient fine-tuning (PEFT) of pre-trained language models has\nrecently demonstrated remarkable achievements, effectively matching the\nperformance of full fine-tuning while utilizing significantly fewer trainable\nparameters, and consequently addressing the storage and communication\nconstraints. No... | 2305.16742 | 2203.05948 | 2305.16742_2203.05948 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Parameter-efficient fine-tuning (PEFT) of pre-trained language models has
recently demonstrated remarkable achievements, effectively matching the
performance of full fine-tuning while utilizing significantly fewer trainable
parameters, and consequently addressing the storage and communication
constraints. None... | Paper:
Parameter-efficient fine-tuning (PEFT) of pre-trained language models has
recently demonstrated remarkable achievements, effectively matching the
performance of full fine-tuning while utilizing significantly fewer trainable
parameters, and consequently addressing the storage and communication
constraints. Noneth... | Paper:
Recently, it has been shown that, in spite of the significant performance of
deep neural networks in different fields, those are vulnerable to adversarial
examples. In this paper, we propose a gradient-based adversarial attack against
transformer-based text classifiers. The adversarial perturbation in our method... | Here's an insight: |
[
" Text summarization aims to condense long documents and retain key\ninformation. Critical to the success of a summarization model is the faithful\ninference of latent representations of words or tokens in the source documents.\nMost recent models infer the latent representations with a transformer encoder,\nwhich... | 2203.07586 | 2107.12708 | 2203.07586_2107.12708 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Text summarization aims to condense long documents and retain key
information. Critical to the success of a summarization model is the faithful
inference of latent representations of words or tokens in the source documents.
Most recent models infer the latent representations with a transformer encoder,
which i... | Paper:
Text summarization aims to condense long documents and retain key
information. Critical to the success of a summarization model is the faithful
inference of latent representations of words or tokens in the source documents.
Most recent models infer the latent representations with a transformer encoder,
which is ... | Paper:
Alongside huge volumes of research on deep learning models in NLP in the
recent years, there has been also much work on benchmark datasets needed to
track modeling progress. Question answering and reading comprehension have been
particularly prolific in this regard, with over 80 new datasets appearing in
the pas... | Here's an insight: |
[
" Keeping the performance of language technologies optimal as time passes is of\ngreat practical interest. We study temporal effects on model performance on\ndownstream language tasks, establishing a nuanced terminology for such\ndiscussion and identifying factors essential to conduct a robust study. We\npresent e... | 2111.12790 | 2205.12771 | 2111.12790_2205.12771 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Keeping the performance of language technologies optimal as time passes is of
great practical interest. We study temporal effects on model performance on
downstream language tasks, establishing a nuanced terminology for such
discussion and identifying factors essential to conduct a robust study. We
present exp... | Paper:
Keeping the performance of language technologies optimal as time passes is of
great practical interest. We study temporal effects on model performance on
downstream language tasks, establishing a nuanced terminology for such
discussion and identifying factors essential to conduct a robust study. We
present exper... | Paper:
In an effort to guarantee that machine learning model outputs conform with
human moral values, recent work has begun exploring the possibility of
explicitly training models to learn the difference between right and wrong.
This is typically done in a bottom-up fashion, by exposing the model to
different scenarios... | Here's an insight: |
[
" Unsupervised sentence representation learning is one of the fundamental\nproblems in natural language processing with various downstream applications.\nRecently, contrastive learning has been widely adopted which derives\nhigh-quality sentence representations by pulling similar semantics closer and\npushing diss... | 2305.16726 | 2204.12811 | 2305.16726_2204.12811 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Unsupervised sentence representation learning is one of the fundamental
problems in natural language processing with various downstream applications.
Recently, contrastive learning has been widely adopted which derives
high-quality sentence representations by pulling similar semantics closer and
pushing dissim... | Paper:
Unsupervised sentence representation learning is one of the fundamental
problems in natural language processing with various downstream applications.
Recently, contrastive learning has been widely adopted which derives
high-quality sentence representations by pulling similar semantics closer and
pushing dissimil... | Paper:
Skill Extraction (SE) is an important and widely-studied task useful to gain
insights into labor market dynamics. However, there is a lacuna of datasets and
annotation guidelines; available datasets are few and contain crowd-sourced
labels on the span-level or labels from a predefined skill inventory. To
address... | Here's an insight: |
[
" Importance: Social determinants of health (SDOH) are known to be associated\nwith increased risk of suicidal behaviors, but few studies utilized SDOH from\nunstructured electronic health record (EHR) notes.\n Objective: To investigate associations between suicide and recent SDOH,\nidentified using structured an... | 2212.05546 | 2210.17027 | 2212.05546_2210.17027 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Importance: Social determinants of health (SDOH) are known to be associated
with increased risk of suicidal behaviors, but few studies utilized SDOH from
unstructured electronic health record (EHR) notes.
Objective: To investigate associations between suicide and recent SDOH,
identified using structured and ... | Paper:
Importance: Social determinants of health (SDOH) are known to be associated
with increased risk of suicidal behaviors, but few studies utilized SDOH from
unstructured electronic health record (EHR) notes.
Objective: To investigate associations between suicide and recent SDOH,
identified using structured and un... | Paper:
Direct speech-to-speech translation (S2ST) is an attractive research topic
with many advantages compared to cascaded S2ST. However, direct S2ST suffers
from the data scarcity problem because the corpora from speech of the source
language to speech of the target language are very rare. To address this issue,
we p... | Here's an insight: |
[
" Neuron analysis provides insights into how knowledge is structured in\nrepresentations and discovers the role of neurons in the network. In addition\nto developing an understanding of our models, neuron analysis enables various\napplications such as debiasing, domain adaptation and architectural search. We\npres... | 2305.17073 | 2209.07084 | 2305.17073_2209.07084 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Neuron analysis provides insights into how knowledge is structured in
representations and discovers the role of neurons in the network. In addition
to developing an understanding of our models, neuron analysis enables various
applications such as debiasing, domain adaptation and architectural search. We
presen... | Paper:
Neuron analysis provides insights into how knowledge is structured in
representations and discovers the role of neurons in the network. In addition
to developing an understanding of our models, neuron analysis enables various
applications such as debiasing, domain adaptation and architectural search. We
present ... | Paper:
Knowledge graphs (KGs) that modelings the world knowledge as structural
triples are inevitably incomplete. Such problems still exist for multimodal
knowledge graphs (MMKGs). Thus, knowledge graph completion (KGC) is of great
importance to predict the missing triples in the existing KGs. As for the
existing KGC m... | Here's an insight: |
[
" We present a new fact-checking benchmark, Check-COVID, that requires systems\nto verify claims about COVID-19 from news using evidence from scientific\narticles. This approach to fact-checking is particularly challenging as it\nrequires checking internet text written in everyday language against evidence\nfrom j... | 2305.18265 | 2303.10311 | 2305.18265_2303.10311 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
We present a new fact-checking benchmark, Check-COVID, that requires systems
to verify claims about COVID-19 from news using evidence from scientific
articles. This approach to fact-checking is particularly challenging as it
requires checking internet text written in everyday language against evidence
from jou... | Paper:
We present a new fact-checking benchmark, Check-COVID, that requires systems
to verify claims about COVID-19 from news using evidence from scientific
articles. This approach to fact-checking is particularly challenging as it
requires checking internet text written in everyday language against evidence
from journ... | Paper:
Recently, social media platforms are heavily moderated to prevent the spread
of online hate speech, which is usually fertile in toxic words and is directed
toward an individual or a community. Owing to such heavy moderation, newer and
more subtle techniques are being deployed. One of the most striking among thes... | Here's an insight: |
[
" Language models have become very popular recently and many claims have been\nmade about their abilities, including for commonsense reasoning. Given the\nincreasingly better results of current language models on previous static\nbenchmarks for commonsense reasoning, we explore an alternative dialectical\nevaluati... | 2304.11164 | 2210.08817 | 2304.11164_2210.08817 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Language models have become very popular recently and many claims have been
made about their abilities, including for commonsense reasoning. Given the
increasingly better results of current language models on previous static
benchmarks for commonsense reasoning, we explore an alternative dialectical
evaluation... | Paper:
Language models have become very popular recently and many claims have been
made about their abilities, including for commonsense reasoning. Given the
increasingly better results of current language models on previous static
benchmarks for commonsense reasoning, we explore an alternative dialectical
evaluation. ... | Paper:
To facilitate conversational question answering (CQA) over hybrid contexts in
finance, we present a new dataset, named PACIFIC. Compared with existing CQA
datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical
reasoning, and (iii) hybrid context of tables and text. A new task is defined
a... | Here's an insight: |
[
" Few-shot named entity recognition (NER) targets generalizing to unseen labels\nand/or domains with few labeled examples. Existing metric learning methods\ncompute token-level similarities between query and support sets, but are not\nable to fully incorporate label semantics into modeling. To address this issue,\... | 2211.04337 | 2204.03035 | 2211.04337_2204.03035 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Few-shot named entity recognition (NER) targets generalizing to unseen labels
and/or domains with few labeled examples. Existing metric learning methods
compute token-level similarities between query and support sets, but are not
able to fully incorporate label semantics into modeling. To address this issue,
w... | Paper:
Few-shot named entity recognition (NER) targets generalizing to unseen labels
and/or domains with few labeled examples. Existing metric learning methods
compute token-level similarities between query and support sets, but are not
able to fully incorporate label semantics into modeling. To address this issue,
we ... | Paper:
Applying methods in natural language processing on electronic health records
(EHR) data is a growing field. Existing corpus and annotation focus on modeling
textual features and relation prediction. However, there is a paucity of
annotated corpus built to model clinical diagnostic thinking, a process
involving t... | Here's an insight: |
[
" The Natural Language for Optimization (NL4Opt) Competition was created to\ninvestigate methods of extracting the meaning and formulation of an\noptimization problem based on its text description. Specifically, the goal of\nthe competition is to increase the accessibility and usability of optimization\nsolvers by... | 2303.08233 | 2204.05610 | 2303.08233_2204.05610 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The Natural Language for Optimization (NL4Opt) Competition was created to
investigate methods of extracting the meaning and formulation of an
optimization problem based on its text description. Specifically, the goal of
the competition is to increase the accessibility and usability of optimization
solvers by a... | Paper:
The Natural Language for Optimization (NL4Opt) Competition was created to
investigate methods of extracting the meaning and formulation of an
optimization problem based on its text description. Specifically, the goal of
the competition is to increase the accessibility and usability of optimization
solvers by all... | Paper:
Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in
producing rational and factual responses. However, to establish long-term
relationships with users, the KDG model needs the capability to generate
responses in a desired style or attribute. Thus, we study a new problem:
Stylized Knowledge-... | Here's an insight: |
[
" Recent approaches of computer vision utilize deep learning methods as they\nperform quite well if training and testing domains follow the same underlying\ndata distribution. However, it has been shown that minor variations in the\nimages that occur when using these methods in the real world can lead to\nunpredic... | 2201.11794 | 2202.12205 | 2201.11794_2202.12205 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Recent approaches of computer vision utilize deep learning methods as they
perform quite well if training and testing domains follow the same underlying
data distribution. However, it has been shown that minor variations in the
images that occur when using these methods in the real world can lead to
unpredicta... | Paper:
Recent approaches of computer vision utilize deep learning methods as they
perform quite well if training and testing domains follow the same underlying
data distribution. However, it has been shown that minor variations in the
images that occur when using these methods in the real world can lead to
unpredictabl... | Paper:
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstrac... | Here's an insight: |
[
" Large pretrained Transformer language models have been shown to exhibit\nzero-shot generalization, i.e. they can perform a wide variety of tasks that\nthey were not explicitly trained on. However, the architectures and pretraining\nobjectives used across state-of-the-art models differ significantly, and there\nh... | 2204.05832 | 2105.08481 | 2204.05832_2105.08481 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Large pretrained Transformer language models have been shown to exhibit
zero-shot generalization, i.e. they can perform a wide variety of tasks that
they were not explicitly trained on. However, the architectures and pretraining
objectives used across state-of-the-art models differ significantly, and there
has... | Paper:
Large pretrained Transformer language models have been shown to exhibit
zero-shot generalization, i.e. they can perform a wide variety of tasks that
they were not explicitly trained on. However, the architectures and pretraining
objectives used across state-of-the-art models differ significantly, and there
has b... | Paper:
Given a video, video grounding aims to retrieve a temporal moment that
semantically corresponds to a language query. In this work, we propose a
Parallel Attention Network with Sequence matching (SeqPAN) to address the
challenges in this task: multi-modal representation learning, and target moment
boundary predic... | Here's an insight: |
[
" Large language models generate fluent texts and can follow natural language\ninstructions to solve a wide range of tasks without task-specific training.\nNevertheless, it is notoriously difficult to control their generation to\nsatisfy the various constraints required by different applications. In this\nwork, we... | 2304.14293 | 2209.09480 | 2304.14293_2209.09480 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Large language models generate fluent texts and can follow natural language
instructions to solve a wide range of tasks without task-specific training.
Nevertheless, it is notoriously difficult to control their generation to
satisfy the various constraints required by different applications. In this
work, we p... | Paper:
Large language models generate fluent texts and can follow natural language
instructions to solve a wide range of tasks without task-specific training.
Nevertheless, it is notoriously difficult to control their generation to
satisfy the various constraints required by different applications. In this
work, we pre... | Paper:
Deep Neural Networks (DNNs) are generally designed as sequentially cascaded
differentiable blocks/layers with a prediction module connected only to its
last layer. DNNs can be attached with prediction modules at multiple points
along the backbone where inference can stop at an intermediary stage without
passing ... | Here's an insight: |
[
" A significant share of political discourse occurs online on social media\nplatforms. Policymakers and researchers try to understand the role of social\nmedia design in shaping the quality of political discourse around the globe. In\nthe past decades, scholarship on political discourse theory has produced\ndistin... | 2302.09540 | 2110.08345 | 2302.09540_2110.08345 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
A significant share of political discourse occurs online on social media
platforms. Policymakers and researchers try to understand the role of social
media design in shaping the quality of political discourse around the globe. In
the past decades, scholarship on political discourse theory has produced
distinct... | Paper:
A significant share of political discourse occurs online on social media
platforms. Policymakers and researchers try to understand the role of social
media design in shaping the quality of political discourse around the globe. In
the past decades, scholarship on political discourse theory has produced
distinct c... | Paper:
Existing studies on semantic parsing focus primarily on mapping a
natural-language utterance to a corresponding logical form in one turn.
However, because natural language can contain a great deal of ambiguity and
variability, this is a difficult challenge. In this work, we investigate an
interactive semantic pa... | Here's an insight: |
[
" Finding word boundaries in continuous speech is challenging as there is\nlittle or no equivalent of a 'space' delimiter between words. Popular Bayesian\nnon-parametric models for text segmentation use a Dirichlet process to jointly\nsegment sentences and build a lexicon of word types. We introduce DP-Parse,\nwhi... | 2206.11332 | 2203.09148 | 2206.11332_2203.09148 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Finding word boundaries in continuous speech is challenging as there is
little or no equivalent of a 'space' delimiter between words. Popular Bayesian
non-parametric models for text segmentation use a Dirichlet process to jointly
segment sentences and build a lexicon of word types. We introduce DP-Parse,
which... | Paper:
Finding word boundaries in continuous speech is challenging as there is
little or no equivalent of a 'space' delimiter between words. Popular Bayesian
non-parametric models for text segmentation use a Dirichlet process to jointly
segment sentences and build a lexicon of word types. We introduce DP-Parse,
which u... | Paper:
This paper presents a speech intelligibility model based on automatic speech
recognition (ASR), combining phoneme probabilities from deep neural networks
(DNN) and a performance measure that estimates the word error rate from these
probabilities. This model does not require the clean speech reference nor the
wor... | Here's an insight: |
[
" Chain-of-Thought (CoT) prompting can dramatically improve the multi-step\nreasoning abilities of large language models (LLMs). CoT explicitly encourages\nthe LLM to generate intermediate rationales for solving a problem, by providing\na series of reasoning steps in the demonstrations. Despite its success, there\... | 2212.10001 | 2205.06072 | 2212.10001_2205.06072 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Chain-of-Thought (CoT) prompting can dramatically improve the multi-step
reasoning abilities of large language models (LLMs). CoT explicitly encourages
the LLM to generate intermediate rationales for solving a problem, by providing
a series of reasoning steps in the demonstrations. Despite its success, there
i... | Paper:
Chain-of-Thought (CoT) prompting can dramatically improve the multi-step
reasoning abilities of large language models (LLMs). CoT explicitly encourages
the LLM to generate intermediate rationales for solving a problem, by providing
a series of reasoning steps in the demonstrations. Despite its success, there
is ... | Paper:
Semantic relatedness between words is one of the core concepts in natural
language processing, thus making semantic evaluation an important task. In this
paper, we present a semantic model evaluation dataset: SimRelUz - a collection
of similarity and relatedness scores of word pairs for the low-resource Uzbek
la... | Here's an insight: |
[
" This paper discusses OpenAIs ChatGPT, a generative pre-trained transformer,\nwhich uses natural language processing to fulfill text-based user requests\n(i.e., a chatbot). The history and principles behind ChatGPT and similar models\nare discussed. This technology is then discussed in relation to its potential\n... | 2303.13367 | 2109.02707 | 2303.13367_2109.02707 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
This paper discusses OpenAIs ChatGPT, a generative pre-trained transformer,
which uses natural language processing to fulfill text-based user requests
(i.e., a chatbot). The history and principles behind ChatGPT and similar models
are discussed. This technology is then discussed in relation to its potential
im... | Paper:
This paper discusses OpenAIs ChatGPT, a generative pre-trained transformer,
which uses natural language processing to fulfill text-based user requests
(i.e., a chatbot). The history and principles behind ChatGPT and similar models
are discussed. This technology is then discussed in relation to its potential
impa... | Paper:
We study a new problem setting of information extraction (IE), referred to as
text-to-table. In text-to-table, given a text, one creates a table or several
tables expressing the main content of the text, while the model is learned from
text-table pair data. The problem setting differs from those of the existing
... | Here's an insight: |
[
" Text-to-image models offer unprecedented freedom to guide creation through\nnatural language. Yet, it is unclear how such freedom can be exercised to\ngenerate images of specific unique concepts, modify their appearance, or\ncompose them in new roles and novel scenes. In other words, we ask: how can we\nuse lang... | 2208.01618 | 2205.00241 | 2208.01618_2205.00241 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Text-to-image models offer unprecedented freedom to guide creation through
natural language. Yet, it is unclear how such freedom can be exercised to
generate images of specific unique concepts, modify their appearance, or
compose them in new roles and novel scenes. In other words, we ask: how can we
use langua... | Paper:
Text-to-image models offer unprecedented freedom to guide creation through
natural language. Yet, it is unclear how such freedom can be exercised to
generate images of specific unique concepts, modify their appearance, or
compose them in new roles and novel scenes. In other words, we ask: how can we
use language... | Paper:
Most previous studies aim at extracting events from a single sentence, while
document-level event extraction still remains under-explored. In this paper, we
focus on extracting event arguments from an entire document, which mainly faces
two critical problems: a) the long-distance dependency between trigger and
a... | Here's an insight: |
[
" Synthesizing QA pairs with a question generator (QG) on the target domain has\nbecome a popular approach for domain adaptation of question answering (QA)\nmodels. Since synthetic questions are often noisy in practice, existing work\nadapts scores from a pretrained QA (or QG) model as criteria to select\nhigh-qua... | 2203.08926 | 2305.11411 | 2203.08926_2305.11411 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Synthesizing QA pairs with a question generator (QG) on the target domain has
become a popular approach for domain adaptation of question answering (QA)
models. Since synthetic questions are often noisy in practice, existing work
adapts scores from a pretrained QA (or QG) model as criteria to select
high-quali... | Paper:
Synthesizing QA pairs with a question generator (QG) on the target domain has
become a popular approach for domain adaptation of question answering (QA)
models. Since synthetic questions are often noisy in practice, existing work
adapts scores from a pretrained QA (or QG) model as criteria to select
high-quality... | Paper:
How can speech-to-text translation (ST) perform as well as machine
translation (MT)? The key point is to bridge the modality gap between speech
and text so that useful MT techniques can be applied to ST. Recently, the
approach of representing speech with unsupervised discrete units yields a new
way to ease the m... | Here's an insight: |
[
" Social media platforms have transformed traditional communication methods by\nallowing users worldwide to communicate instantly, openly, and frequently.\nPeople use social media to express their opinion and share their personal\nstories and struggles. Negative feelings that express hardship, thoughts of\ndeath, ... | 2201.10515 | 2203.02966 | 2201.10515_2203.02966 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Social media platforms have transformed traditional communication methods by
allowing users worldwide to communicate instantly, openly, and frequently.
People use social media to express their opinion and share their personal
stories and struggles. Negative feelings that express hardship, thoughts of
death, an... | Paper:
Social media platforms have transformed traditional communication methods by
allowing users worldwide to communicate instantly, openly, and frequently.
People use social media to express their opinion and share their personal
stories and struggles. Negative feelings that express hardship, thoughts of
death, and ... | Paper:
Temporal sentence grounding aims to localize a target segment in an untrimmed
video semantically according to a given sentence query. Most previous works
focus on learning frame-level features of each whole frame in the entire video,
and directly match them with the textual information. Such frame-level feature
... | Here's an insight: |
[
" Fine-tuning large language models for different tasks can be costly and\ninefficient, and even methods that reduce the number of tuned parameters still\nrequire full gradient-based optimization. We propose HyperTuning, a novel\napproach to model adaptation that uses a hypermodel to generate task-specific\nparame... | 2211.12485 | 2212.14518 | 2211.12485_2212.14518 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Fine-tuning large language models for different tasks can be costly and
inefficient, and even methods that reduce the number of tuned parameters still
require full gradient-based optimization. We propose HyperTuning, a novel
approach to model adaptation that uses a hypermodel to generate task-specific
paramete... | Paper:
Fine-tuning large language models for different tasks can be costly and
inefficient, and even methods that reduce the number of tuned parameters still
require full gradient-based optimization. We propose HyperTuning, a novel
approach to model adaptation that uses a hypermodel to generate task-specific
parameters... | Paper:
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in
text-to-speech (TTS) synthesis because of their strong capability of generating
high-fidelity samples. However, their iterative refinement process in
high-dimensional data space results in slow inference speed, which restricts
their application in ... | Here's an insight: |
[
" This paper introduces Doc2Bot, a novel dataset for building machines that\nhelp users seek information via conversations. This is of particular interest\nfor companies and organizations that own a large number of manuals or\ninstruction books. Despite its potential, the nature of our task poses several\nchalleng... | 2210.11060 | 1708.08615 | 2210.11060_1708.08615 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
This paper introduces Doc2Bot, a novel dataset for building machines that
help users seek information via conversations. This is of particular interest
for companies and organizations that own a large number of manuals or
instruction books. Despite its potential, the nature of our task poses several
challenges... | Paper:
This paper introduces Doc2Bot, a novel dataset for building machines that
help users seek information via conversations. This is of particular interest
for companies and organizations that own a large number of manuals or
instruction books. Despite its potential, the nature of our task poses several
challenges: ... | Paper:
Recent work in automatic recognition of conversational telephone speech (CTS)
has achieved accuracy levels comparable to human transcribers, although there
is some debate how to precisely quantify human performance on this task, using
the NIST 2000 CTS evaluation set. This raises the question what systematic
dif... | Here's an insight: |
[
" Entity alignment (EA) is a fundamental data integration task that identifies\nequivalent entities between different knowledge graphs (KGs). Temporal\nKnowledge graphs (TKGs) extend traditional knowledge graphs by introducing\ntimestamps, which have received increasing attention. State-of-the-art\ntime-aware EA s... | 2302.00796 | 2305.11442 | 2302.00796_2305.11442 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Entity alignment (EA) is a fundamental data integration task that identifies
equivalent entities between different knowledge graphs (KGs). Temporal
Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing
timestamps, which have received increasing attention. State-of-the-art
time-aware EA stu... | Paper:
Entity alignment (EA) is a fundamental data integration task that identifies
equivalent entities between different knowledge graphs (KGs). Temporal
Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing
timestamps, which have received increasing attention. State-of-the-art
time-aware EA studi... | Paper:
Existing solutions to zero-shot text classification either conduct prompting
with pre-trained language models, which is sensitive to the choices of
templates, or rely on large-scale annotated data of relevant tasks for
meta-tuning. In this work, we propose a new paradigm based on self-supervised
learning to solv... | Here's an insight: |
[
" In this paper, we propose a novel method based on character\nsequence-to-sequence models to correct documents already processed with Optical\nCharacter Recognition (OCR) systems. The main contribution of this paper is a\nset of strategies to accurately process strings much longer than the ones used\nto train the... | 2109.06264 | 2305.16504 | 2109.06264_2305.16504 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
In this paper, we propose a novel method based on character
sequence-to-sequence models to correct documents already processed with Optical
Character Recognition (OCR) systems. The main contribution of this paper is a
set of strategies to accurately process strings much longer than the ones used
to train the s... | Paper:
In this paper, we propose a novel method based on character
sequence-to-sequence models to correct documents already processed with Optical
Character Recognition (OCR) systems. The main contribution of this paper is a
set of strategies to accurately process strings much longer than the ones used
to train the seq... | Paper:
Recent studies on software tool manipulation with large language models
(LLMs) mostly rely on closed model APIs. The industrial adoption of these
models is substantially constrained due to the security and robustness risks in
exposing information to closed LLM API services. In this paper, we ask can we
enhance o... | Here's an insight: |
[
" Multi-lingual speech recognition aims to distinguish linguistic expressions\nin different languages and integrate acoustic processing simultaneously. In\ncontrast, current multi-lingual speech recognition research follows a\nlanguage-aware paradigm, mainly targeted to improve recognition performance\nrather than... | 2302.13750 | 2110.08352 | 2302.13750_2110.08352 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Multi-lingual speech recognition aims to distinguish linguistic expressions
in different languages and integrate acoustic processing simultaneously. In
contrast, current multi-lingual speech recognition research follows a
language-aware paradigm, mainly targeted to improve recognition performance
rather than d... | Paper:
Multi-lingual speech recognition aims to distinguish linguistic expressions
in different languages and integrate acoustic processing simultaneously. In
contrast, current multi-lingual speech recognition research follows a
language-aware paradigm, mainly targeted to improve recognition performance
rather than dis... | Paper:
From wearables to powerful smart devices, modern automatic speech recognition
(ASR) models run on a variety of edge devices with different computational
budgets. To navigate the Pareto front of model accuracy vs model size,
researchers are trapped in a dilemma of optimizing model accuracy by training
and fine-tu... | Here's an insight: |
[
" The massive amounts of digitized historical documents acquired over the last\ndecades naturally lend themselves to automatic processing and exploration.\nResearch work seeking to automatically process facsimiles and extract\ninformation thereby are multiplying with, as a first essential step, document\nlayout an... | 2002.06144 | 2305.01795 | 2002.06144_2305.01795 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout anal... | Paper:
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout analys... | Paper:
Embodied agents have achieved prominent performance in following human
instructions to complete tasks. However, the potential of providing
instructions informed by texts and images to assist humans in completing tasks
remains underexplored. To uncover this capability, we present the multimodal
procedural plannin... | Here's an insight: |
[
" A method to perform offline and online speaker diarization for an unlimited\nnumber of speakers is described in this paper. End-to-end neural diarization\n(EEND) has achieved overlap-aware speaker diarization by formulating it as a\nmulti-label classification problem. It has also been extended for a flexible\nnu... | 2206.02432 | 2302.11989 | 2206.02432_2302.11989 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
A method to perform offline and online speaker diarization for an unlimited
number of speakers is described in this paper. End-to-end neural diarization
(EEND) has achieved overlap-aware speaker diarization by formulating it as a
multi-label classification problem. It has also been extended for a flexible
numb... | Paper:
A method to perform offline and online speaker diarization for an unlimited
number of speakers is described in this paper. End-to-end neural diarization
(EEND) has achieved overlap-aware speaker diarization by formulating it as a
multi-label classification problem. It has also been extended for a flexible
number... | Paper:
Deep neural network based speech enhancement technique focuses on learning a
noisy-to-clean transformation supervised by paired training data. However, the
task-specific evaluation metric (e.g., PESQ) is usually non-differentiable and
can not be directly constructed in the training criteria. This mismatch betwee... | Here's an insight: |
[
" Generative modeling has been the dominant approach for large-scale\npretraining and zero-shot generalization. In this work, we challenge this\nconvention by showing that discriminative approaches perform substantially\nbetter than generative ones on a large number of NLP tasks. Technically, we\ntrain a single di... | 2211.08099 | 2211.02519 | 2211.08099_2211.02519 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Generative modeling has been the dominant approach for large-scale
pretraining and zero-shot generalization. In this work, we challenge this
convention by showing that discriminative approaches perform substantially
better than generative ones on a large number of NLP tasks. Technically, we
train a single disc... | Paper:
Generative modeling has been the dominant approach for large-scale
pretraining and zero-shot generalization. In this work, we challenge this
convention by showing that discriminative approaches perform substantially
better than generative ones on a large number of NLP tasks. Technically, we
train a single discri... | Paper:
Transformer models have achieved great success across many NLP problems.
However, previous studies in automated ICD coding concluded that these models
fail to outperform some of the earlier solutions such as CNN-based models. In
this paper we challenge this conclusion. We present a simple and scalable
method to ... | Here's an insight: |
[
" Using prompts to explore the knowledge contained within pre-trained language\nmodels for downstream tasks has now become an active topic. Current prompt\ntuning methods mostly convert the downstream tasks to masked language modeling\nproblems by adding cloze-style phrases and mapping all labels to verbalizations... | 2210.12435 | 2201.05363 | 2210.12435_2201.05363 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Using prompts to explore the knowledge contained within pre-trained language
models for downstream tasks has now become an active topic. Current prompt
tuning methods mostly convert the downstream tasks to masked language modeling
problems by adding cloze-style phrases and mapping all labels to verbalizations
... | Paper:
Using prompts to explore the knowledge contained within pre-trained language
models for downstream tasks has now become an active topic. Current prompt
tuning methods mostly convert the downstream tasks to masked language modeling
problems by adding cloze-style phrases and mapping all labels to verbalizations
wi... | Paper:
Multitask learning often helps improve the performance of related tasks as
these often have inter-dependence on each other and perform better when solved
in a joint framework. In this paper, we present a deep multitask learning
framework that jointly performs polarity and subjective detection. We propose
an atte... | Here's an insight: |
[
" Text embeddings are useful features in many applications such as semantic\nsearch and computing text similarity. Previous work typically trains models\ncustomized for different use cases, varying in dataset choice, training\nobjective and model architecture. In this work, we show that contrastive\npre-training o... | 2201.10005 | 2207.14087 | 2201.10005_2207.14087 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Text embeddings are useful features in many applications such as semantic
search and computing text similarity. Previous work typically trains models
customized for different use cases, varying in dataset choice, training
objective and model architecture. In this work, we show that contrastive
pre-training on ... | Paper:
Text embeddings are useful features in many applications such as semantic
search and computing text similarity. Previous work typically trains models
customized for different use cases, varying in dataset choice, training
objective and model architecture. In this work, we show that contrastive
pre-training on un... | Paper:
Multimodal sentiment analysis and depression estimation are two important
research topics that aim to predict human mental states using multimodal data.
Previous research has focused on developing effective fusion strategies for
exchanging and integrating mind-related information from different modalities.
Some ... | Here's an insight: |
[
" Recent works that revealed the vulnerability of dialogue state tracking (DST)\nmodels to distributional shifts have made holistic comparisons on robustness\nand qualitative analyses increasingly important for understanding their\nrelative performance. We present our findings from standardized and\ncomprehensive ... | 2112.08321 | 2306.03078 | 2112.08321_2306.03078 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Recent works that revealed the vulnerability of dialogue state tracking (DST)
models to distributional shifts have made holistic comparisons on robustness
and qualitative analyses increasingly important for understanding their
relative performance. We present our findings from standardized and
comprehensive DS... | Paper:
Recent works that revealed the vulnerability of dialogue state tracking (DST)
models to distributional shifts have made holistic comparisons on robustness
and qualitative analyses increasingly important for understanding their
relative performance. We present our findings from standardized and
comprehensive DST ... | Paper:
Recent advances in large language model (LLM) pretraining have led to
high-quality LLMs with impressive abilities. By compressing such LLMs via
quantization to 3-4 bits per parameter, they can fit into memory-limited
devices such as laptops and mobile phones, enabling personalized use. However,
quantization down... | Here's an insight: |
[
" Construction of human-curated annotated datasets for abstractive text\nsummarization (ATS) is very time-consuming and expensive because creating each\ninstance requires a human annotator to read a long document and compose a\nshorter summary that would preserve the key information relayed by the original\ndocume... | 2301.03252 | 2210.04963 | 2301.03252_2210.04963 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Construction of human-curated annotated datasets for abstractive text
summarization (ATS) is very time-consuming and expensive because creating each
instance requires a human annotator to read a long document and compose a
shorter summary that would preserve the key information relayed by the original
document... | Paper:
Construction of human-curated annotated datasets for abstractive text
summarization (ATS) is very time-consuming and expensive because creating each
instance requires a human annotator to read a long document and compose a
shorter summary that would preserve the key information relayed by the original
document. ... | Paper:
Human fixation patterns have been shown to correlate strongly with
Transformer-based attention. Those correlation analyses are usually carried out
without taking into account individual differences between participants and are
mostly done on monolingual datasets making it difficult to generalise findings.
In thi... | Here's an insight: |
[
" In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel\napproach aimed at improving the problem-solving capabilities of auto-regressive\nlarge language models (LLMs). The ToT technique is inspired by the human mind's\napproach for solving complex reasoning tasks through trial and error. In this... | 2305.08291 | 2305.19280 | 2305.08291_2305.19280 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
approach aimed at improving the problem-solving capabilities of auto-regressive
large language models (LLMs). The ToT technique is inspired by the human mind's
approach for solving complex reasoning tasks through trial and error. In this
... | Paper:
In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
approach aimed at improving the problem-solving capabilities of auto-regressive
large language models (LLMs). The ToT technique is inspired by the human mind's
approach for solving complex reasoning tasks through trial and error. In this
pr... | Paper:
In diagnosing challenging conditions such as Alzheimer's disease (AD),
imaging is an important reference. Non-imaging patient data such as patient
information, genetic data, medication information, cognitive and memory tests
also play a very important role in diagnosis. Effect. However, limited by the
ability of... | Here's an insight: |
[
" Factorized layers--operations parameterized by products of two or more\nmatrices--occur in a variety of deep learning contexts, including compressed\nmodel training, certain types of knowledge distillation, and multi-head\nself-attention architectures. We study how to initialize and regularize deep\nnets contain... | 2105.01029 | 2206.13947 | 2105.01029_2206.13947 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Factorized layers--operations parameterized by products of two or more
matrices--occur in a variety of deep learning contexts, including compressed
model training, certain types of knowledge distillation, and multi-head
self-attention architectures. We study how to initialize and regularize deep
nets containin... | Paper:
Factorized layers--operations parameterized by products of two or more
matrices--occur in a variety of deep learning contexts, including compressed
model training, certain types of knowledge distillation, and multi-head
self-attention architectures. We study how to initialize and regularize deep
nets containing ... | Paper:
State space models have shown to be effective at modeling long range
dependencies, specially on sequence classification tasks. In this work we focus
on autoregressive sequence modeling over English books, Github source code and
ArXiv mathematics articles. Based on recent developments around the
effectiveness of ... | Here's an insight: |
[
" The relevance of the Key Information Extraction (KIE) task is increasingly\nimportant in natural language processing problems. But there are still only a\nfew well-defined problems that serve as benchmarks for solutions in this area.\nTo bridge this gap, we introduce two new datasets (Kleister NDA and Kleister\n... | 2105.05796 | 1906.01926 | 2105.05796_1906.01926 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The relevance of the Key Information Extraction (KIE) task is increasingly
important in natural language processing problems. But there are still only a
few well-defined problems that serve as benchmarks for solutions in this area.
To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister
Ch... | Paper:
The relevance of the Key Information Extraction (KIE) task is increasingly
important in natural language processing problems. But there are still only a
few well-defined problems that serve as benchmarks for solutions in this area.
To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister
Char... | Paper:
Cross-lingual word embeddings encode the meaning of words from different
languages into a shared low-dimensional space. An important requirement for
many downstream tasks is that word similarity should be independent of language
- i.e., word vectors within one language should not be more similar to each
other th... | Here's an insight: |
[
" Recent work has shown that language models (LMs) trained with multi-task\n\\textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and\nfew-shot settings with improved performance compared to prompt tuning. MTIL\nillustrates that LMs can extract and use information about the task from\ninstru... | 2210.11617 | 2306.00024 | 2210.11617_2306.00024 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Recent work has shown that language models (LMs) trained with multi-task
\textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and
few-shot settings with improved performance compared to prompt tuning. MTIL
illustrates that LMs can extract and use information about the task from
instructi... | Paper:
Recent work has shown that language models (LMs) trained with multi-task
\textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and
few-shot settings with improved performance compared to prompt tuning. MTIL
illustrates that LMs can extract and use information about the task from
instruction... | Paper:
Extracting patient information from unstructured text is a critical task in
health decision-support and clinical research. Large language models (LLMs)
have shown the potential to accelerate clinical curation via few-shot
in-context learning, in contrast to supervised learning which requires much
more costly hum... | Here's an insight: |
[
" Deep neural networks (DNNs) have achieved unprecedented success in the field\nof artificial intelligence (AI), including computer vision, natural language\nprocessing and speech recognition. However, their superior performance comes at\nthe considerable cost of computational complexity, which greatly hinders the... | 2204.11786 | 2212.13196 | 2204.11786_2212.13196 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Deep neural networks (DNNs) have achieved unprecedented success in the field
of artificial intelligence (AI), including computer vision, natural language
processing and speech recognition. However, their superior performance comes at
the considerable cost of computational complexity, which greatly hinders thei... | Paper:
Deep neural networks (DNNs) have achieved unprecedented success in the field
of artificial intelligence (AI), including computer vision, natural language
processing and speech recognition. However, their superior performance comes at
the considerable cost of computational complexity, which greatly hinders their
... | Paper:
Biological systems in nature have evolved for millions of years to adapt and
survive the environment. Many features they developed can be inspirational and
beneficial for solving technical problems in modern industries. This leads to a
specific form of design-by-analogy called bio-inspired design (BID). Although... | Here's an insight: |
[
" The Generative Pre-trained Transformer (GPT) represents a notable\nbreakthrough in the domain of natural language processing, which is propelling\nus toward the development of machines that can understand and communicate using\nlanguage in a manner that closely resembles that of humans. GPT is based on the\ntran... | 2305.10435 | 2112.08321 | 2305.10435_2112.08321 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The Generative Pre-trained Transformer (GPT) represents a notable
breakthrough in the domain of natural language processing, which is propelling
us toward the development of machines that can understand and communicate using
language in a manner that closely resembles that of humans. GPT is based on the
transf... | Paper:
The Generative Pre-trained Transformer (GPT) represents a notable
breakthrough in the domain of natural language processing, which is propelling
us toward the development of machines that can understand and communicate using
language in a manner that closely resembles that of humans. GPT is based on the
transfor... | Paper:
Recent works that revealed the vulnerability of dialogue state tracking (DST)
models to distributional shifts have made holistic comparisons on robustness
and qualitative analyses increasingly important for understanding their
relative performance. We present our findings from standardized and
comprehensive DST ... | Here's an insight: |
[
" To explain NLP models a popular approach is to use importance measures, such\nas attention, which inform input tokens are important for making a prediction.\nHowever, an open question is how well these explanations accurately reflect a\nmodel's logic, a property called faithfulness.\n To answer this question, w... | 2110.08412 | 2211.11152 | 2110.08412_2211.11152 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
To explain NLP models a popular approach is to use importance measures, such
as attention, which inform input tokens are important for making a prediction.
However, an open question is how well these explanations accurately reflect a
model's logic, a property called faithfulness.
To answer this question, we ... | Paper:
To explain NLP models a popular approach is to use importance measures, such
as attention, which inform input tokens are important for making a prediction.
However, an open question is how well these explanations accurately reflect a
model's logic, a property called faithfulness.
To answer this question, we pr... | Paper:
Large-scale Transformer models bring significant improvements for various
downstream vision language tasks with a unified architecture. The performance
improvements come with increasing model size, resulting in slow inference speed
and increased cost for severing. While some certain predictions benefit from
the ... | Here's an insight: |
[
" There is mounting evidence that existing neural network models, in particular\nthe very popular sequence-to-sequence architecture, struggle to systematically\ngeneralize to unseen compositions of seen components. We demonstrate that one\nof the reasons hindering compositional generalization relates to\nrepresent... | 2110.04655 | 2201.03533 | 2110.04655_2201.03533 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
There is mounting evidence that existing neural network models, in particular
the very popular sequence-to-sequence architecture, struggle to systematically
generalize to unseen compositions of seen components. We demonstrate that one
of the reasons hindering compositional generalization relates to
representat... | Paper:
There is mounting evidence that existing neural network models, in particular
the very popular sequence-to-sequence architecture, struggle to systematically
generalize to unseen compositions of seen components. We demonstrate that one
of the reasons hindering compositional generalization relates to
representatio... | Paper:
NLP benchmarks have largely focused on short texts, such as sentences and
paragraphs, even though long texts comprise a considerable amount of natural
language in the wild. We introduce SCROLLS, a suite of tasks that require
reasoning over long texts. We examine existing long-text datasets, and handpick
ones whe... | Here's an insight: |
[
" Vision and language navigation (VLN) is a challenging visually-grounded\nlanguage understanding task. Given a natural language navigation instruction, a\nvisual agent interacts with a graph-based environment equipped with panorama\nimages and tries to follow the described route. Most prior work has been\nconduct... | 2203.13838 | 2203.14371 | 2203.13838_2203.14371 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Vision and language navigation (VLN) is a challenging visually-grounded
language understanding task. Given a natural language navigation instruction, a
visual agent interacts with a graph-based environment equipped with panorama
images and tries to follow the described route. Most prior work has been
conducted... | Paper:
Vision and language navigation (VLN) is a challenging visually-grounded
language understanding task. Given a natural language navigation instruction, a
visual agent interacts with a graph-based environment equipped with panorama
images and tries to follow the described route. Most prior work has been
conducted i... | Paper:
This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question
Answering (MCQA) dataset designed to address real-world medical entrance exam
questions. More than 194k high-quality AIIMS \& NEET PG entrance exam MCQs
covering 2.4k healthcare topics and 21 medical subjects are collected with an
average... | Here's an insight: |
[
" A key trait of daily conversations between individuals is the ability to\nexpress empathy towards others, and exploring ways to implement empathy is a\ncrucial step towards human-like dialogue systems. Previous approaches on this\ntopic mainly focus on detecting and utilizing the user's emotion for generating\ne... | 2109.05739 | 2206.07023 | 2109.05739_2206.07023 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
A key trait of daily conversations between individuals is the ability to
express empathy towards others, and exploring ways to implement empathy is a
crucial step towards human-like dialogue systems. Previous approaches on this
topic mainly focus on detecting and utilizing the user's emotion for generating
emp... | Paper:
A key trait of daily conversations between individuals is the ability to
express empathy towards others, and exploring ways to implement empathy is a
crucial step towards human-like dialogue systems. Previous approaches on this
topic mainly focus on detecting and utilizing the user's emotion for generating
empat... | Paper:
Models based on large-pretrained language models, such as S(entence)BERT,
provide effective and efficient sentence embeddings that show high correlation
to human similarity ratings, but lack interpretability. On the other hand,
graph metrics for graph-based meaning representations (e.g., Abstract Meaning
Represe... | Here's an insight: |
[
" Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation\nlanguage model in the GPT series, developed by OpenAI, which promises\nsignificant advancements in the field of natural language processing (NLP). In\nthis research article, we have discussed the features of GPT-4, its potential\napplications... | 2305.03195 | 2110.06634 | 2305.03195_2110.06634 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation
language model in the GPT series, developed by OpenAI, which promises
significant advancements in the field of natural language processing (NLP). In
this research article, we have discussed the features of GPT-4, its potential
applications, ... | Paper:
Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation
language model in the GPT series, developed by OpenAI, which promises
significant advancements in the field of natural language processing (NLP). In
this research article, we have discussed the features of GPT-4, its potential
applications, an... | Paper:
In a recent study of auditory evoked potential (AEP) based brain-computer
interface (BCI), it was shown that, with an encoder-decoder framework, it is
possible to translate human neural activity to speech (T-CAS). However, current
encoder-decoder-based methods achieve T-CAS often with a two-step method where
the... | Here's an insight: |
[
" This paper studies the multimedia problem of temporal sentence grounding\n(TSG), which aims to accurately determine the specific video segment in an\nuntrimmed video according to a given sentence query. Traditional TSG methods\nmainly follow the top-down or bottom-up framework and are not end-to-end. They\nsever... | 2208.14882 | 2305.10010 | 2208.14882_2305.10010 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
This paper studies the multimedia problem of temporal sentence grounding
(TSG), which aims to accurately determine the specific video segment in an
untrimmed video according to a given sentence query. Traditional TSG methods
mainly follow the top-down or bottom-up framework and are not end-to-end. They
severel... | Paper:
This paper studies the multimedia problem of temporal sentence grounding
(TSG), which aims to accurately determine the specific video segment in an
untrimmed video according to a given sentence query. Traditional TSG methods
mainly follow the top-down or bottom-up framework and are not end-to-end. They
severely ... | Paper:
Knowledge distillation has attracted a great deal of interest recently to
compress pre-trained language models. However, existing knowledge distillation
methods suffer from two limitations. First, the student model simply imitates
the teacher's behavior while ignoring the underlying reasoning. Second, these
meth... | Here's an insight: |
[
" Although the vision-and-language pretraining (VLP) equipped cross-modal\nimage-text retrieval (ITR) has achieved remarkable progress in the past two\nyears, it suffers from a major drawback: the ever-increasing size of VLP models\nrestricts its deployment to real-world search scenarios (where the high latency\ni... | 2207.01426 | 2209.00099 | 2207.01426_2209.00099 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Although the vision-and-language pretraining (VLP) equipped cross-modal
image-text retrieval (ITR) has achieved remarkable progress in the past two
years, it suffers from a major drawback: the ever-increasing size of VLP models
restricts its deployment to real-world search scenarios (where the high latency
is ... | Paper:
Although the vision-and-language pretraining (VLP) equipped cross-modal
image-text retrieval (ITR) has achieved remarkable progress in the past two
years, it suffers from a major drawback: the ever-increasing size of VLP models
restricts its deployment to real-world search scenarios (where the high latency
is un... | Paper:
Recent work in natural language processing (NLP) has yielded appealing
results from scaling model parameters and training data; however, using only
scale to improve performance means that resource consumption also grows. Such
resources include data, time, storage, or energy, all of which are naturally
limited an... | Here's an insight: |
[
" Modern embedding-based metrics for evaluation of generated text generally\nfall into one of two paradigms: discriminative metrics that are trained to\ndirectly predict which outputs are of higher quality according to supervised\nhuman annotations, and generative metrics that are trained to evaluate text\nbased o... | 2212.05726 | 2201.08904 | 2212.05726_2201.08904 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Modern embedding-based metrics for evaluation of generated text generally
fall into one of two paradigms: discriminative metrics that are trained to
directly predict which outputs are of higher quality according to supervised
human annotations, and generative metrics that are trained to evaluate text
based on ... | Paper:
Modern embedding-based metrics for evaluation of generated text generally
fall into one of two paradigms: discriminative metrics that are trained to
directly predict which outputs are of higher quality according to supervised
human annotations, and generative metrics that are trained to evaluate text
based on th... | Paper:
Task-oriented dialogue (TOD) systems are required to identify key information
from conversations for the completion of given tasks. Such information is
conventionally specified in terms of intents and slots contained in
task-specific ontology or schemata. Since these schemata are designed by system
developers, t... | Here's an insight: |
[
" We propose a cross-modal attention distillation framework to train a\ndual-encoder model for vision-language understanding tasks, such as visual\nreasoning and visual question answering. Dual-encoder models have a faster\ninference speed than fusion-encoder models and enable the pre-computation of\nimages and te... | 2112.08723 | 2205.13339 | 2112.08723_2205.13339 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
We propose a cross-modal attention distillation framework to train a
dual-encoder model for vision-language understanding tasks, such as visual
reasoning and visual question answering. Dual-encoder models have a faster
inference speed than fusion-encoder models and enable the pre-computation of
images and text... | Paper:
We propose a cross-modal attention distillation framework to train a
dual-encoder model for vision-language understanding tasks, such as visual
reasoning and visual question answering. Dual-encoder models have a faster
inference speed than fusion-encoder models and enable the pre-computation of
images and text d... | Paper:
The related work section is an important component of a scientific paper,
which highlights the contribution of the target paper in the context of the
reference papers. Authors can save their time and effort by using the
automatically generated related work section as a draft to complete the final
related work. M... | Here's an insight: |
[
" Real-life applications, heavily relying on machine learning, such as dialog\nsystems, demand out-of-domain detection methods. Intent classification models\nshould be equipped with a mechanism to distinguish seen intents from unseen\nones so that the dialog agent is capable of rejecting the latter and avoiding\nu... | 2101.03778 | 2203.12990 | 2101.03778_2203.12990 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Real-life applications, heavily relying on machine learning, such as dialog
systems, demand out-of-domain detection methods. Intent classification models
should be equipped with a mechanism to distinguish seen intents from unseen
ones so that the dialog agent is capable of rejecting the latter and avoiding
und... | Paper:
Real-life applications, heavily relying on machine learning, such as dialog
systems, demand out-of-domain detection methods. Intent classification models
should be equipped with a mechanism to distinguish seen intents from unseen
ones so that the dialog agent is capable of rejecting the latter and avoiding
undes... | Paper:
Automated scientific fact checking is difficult due to the complexity of
scientific language and a lack of significant amounts of training data, as
annotation requires domain expertise. To address this challenge, we propose
scientific claim generation, the task of generating one or more atomic and
verifiable cla... | Here's an insight: |
[
" Hedges play an important role in the management of conversational\ninteraction. In peer tutoring, they are notably used by tutors in dyads (pairs\nof interlocutors) experiencing low rapport to tone down the impact of\ninstructions and negative feedback. Pursuing the objective of building a\ntutoring agent that m... | 2306.14911 | 2303.07624 | 2306.14911_2303.07624 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Hedges play an important role in the management of conversational
interaction. In peer tutoring, they are notably used by tutors in dyads (pairs
of interlocutors) experiencing low rapport to tone down the impact of
instructions and negative feedback. Pursuing the objective of building a
tutoring agent that man... | Paper:
Hedges play an important role in the management of conversational
interaction. In peer tutoring, they are notably used by tutors in dyads (pairs
of interlocutors) experiencing low rapport to tone down the impact of
instructions and negative feedback. Pursuing the objective of building a
tutoring agent that manag... | Paper:
Transformer-based end-to-end speech recognition has achieved great success.
However, the large footprint and computational overhead make it difficult to
deploy these models in some real-world applications. Model compression
techniques can reduce the model size and speed up inference, but the compressed
model has... | Here's an insight: |
[
" Meta-embedding (ME) learning is an emerging approach that attempts to learn\nmore accurate word embeddings given existing (source) word embeddings as the\nsole input.\n Due to their ability to incorporate semantics from multiple source embeddings\nin a compact manner with superior performance, ME learning has g... | 2204.11660 | 2112.09174 | 2204.11660_2112.09174 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Meta-embedding (ME) learning is an emerging approach that attempts to learn
more accurate word embeddings given existing (source) word embeddings as the
sole input.
Due to their ability to incorporate semantics from multiple source embeddings
in a compact manner with superior performance, ME learning has gai... | Paper:
Meta-embedding (ME) learning is an emerging approach that attempts to learn
more accurate word embeddings given existing (source) word embeddings as the
sole input.
Due to their ability to incorporate semantics from multiple source embeddings
in a compact manner with superior performance, ME learning has gaine... | Paper:
Long Short-Term Memory (LSTM) and Transformers are two popular neural
architectures used for natural language processing tasks. Theoretical results
show that both are Turing-complete and can represent any context-free language
(CFL).In practice, it is often observed that Transformer models have better
representa... | Here's an insight: |
[
" Recent advances in generative models for language have enabled the creation\nof convincing synthetic text or deepfake text. Prior work has demonstrated the\npotential for misuse of deepfake text to mislead content consumers. Therefore,\ndeepfake text detection, the task of discriminating between human and\nmachi... | 2210.09421 | 2206.01134 | 2210.09421_2206.01134 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Recent advances in generative models for language have enabled the creation
of convincing synthetic text or deepfake text. Prior work has demonstrated the
potential for misuse of deepfake text to mislead content consumers. Therefore,
deepfake text detection, the task of discriminating between human and
machine... | Paper:
Recent advances in generative models for language have enabled the creation
of convincing synthetic text or deepfake text. Prior work has demonstrated the
potential for misuse of deepfake text to mislead content consumers. Therefore,
deepfake text detection, the task of discriminating between human and
machine-g... | Paper:
Building autonomous agents able to grow open-ended repertoires of skills
across their lives is a fundamental goal of artificial intelligence (AI). A
promising developmental approach recommends the design of intrinsically
motivated agents that learn new skills by generating and pursuing their own
goals - autoteli... | Here's an insight: |
[
" Neuron analysis provides insights into how knowledge is structured in\nrepresentations and discovers the role of neurons in the network. In addition\nto developing an understanding of our models, neuron analysis enables various\napplications such as debiasing, domain adaptation and architectural search. We\npres... | 2305.17073 | 2109.00087 | 2305.17073_2109.00087 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Neuron analysis provides insights into how knowledge is structured in
representations and discovers the role of neurons in the network. In addition
to developing an understanding of our models, neuron analysis enables various
applications such as debiasing, domain adaptation and architectural search. We
presen... | Paper:
Neuron analysis provides insights into how knowledge is structured in
representations and discovers the role of neurons in the network. In addition
to developing an understanding of our models, neuron analysis enables various
applications such as debiasing, domain adaptation and architectural search. We
present ... | Paper:
Figurative language is ubiquitous in English. Yet, the vast majority of NLP
research focuses on literal language. Existing text representations by design
rely on compositionality, while figurative language is often non-compositional.
In this paper, we study the interpretation of two non-compositional figurative
... | Here's an insight: |
[
" Task-oriented dialogue is often decomposed into three tasks: understanding\nuser input, deciding actions, and generating a response. While such\ndecomposition might suggest a dedicated model for each sub-task, we find a\nsimple, unified approach leads to state-of-the-art performance on the MultiWOZ\ndataset. Sim... | 2005.00796 | 2210.03797 | 2005.00796_2210.03797 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Task-oriented dialogue is often decomposed into three tasks: understanding
user input, deciding actions, and generating a response. While such
decomposition might suggest a dedicated model for each sub-task, we find a
simple, unified approach leads to state-of-the-art performance on the MultiWOZ
dataset. Simpl... | Paper:
Task-oriented dialogue is often decomposed into three tasks: understanding
user input, deciding actions, and generating a response. While such
decomposition might suggest a dedicated model for each sub-task, we find a
simple, unified approach leads to state-of-the-art performance on the MultiWOZ
dataset. SimpleT... | Paper:
Recent progress in language model pre-training has led to important
improvements in Named Entity Recognition (NER). Nonetheless, this progress has
been mainly tested in well-formatted documents such as news, Wikipedia, or
scientific articles. In social media the landscape is different, in which it
adds another l... | Here's an insight: |
[
" Automatic chart to text summarization is an effective tool for the visually\nimpaired people along with providing precise insights of tabular data in\nnatural language to the user. A large and well-structured dataset is always a\nkey part for data driven models. In this paper, we propose ChartSumm: a\nlarge-scal... | 2304.13620 | 2203.01976 | 2304.13620_2203.01976 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Automatic chart to text summarization is an effective tool for the visually
impaired people along with providing precise insights of tabular data in
natural language to the user. A large and well-structured dataset is always a
key part for data driven models. In this paper, we propose ChartSumm: a
large-scale ... | Paper:
Automatic chart to text summarization is an effective tool for the visually
impaired people along with providing precise insights of tabular data in
natural language to the user. A large and well-structured dataset is always a
key part for data driven models. In this paper, we propose ChartSumm: a
large-scale be... | Paper:
Pre-trained multilingual language models such as mBERT and XLM-R have
demonstrated great potential for zero-shot cross-lingual transfer to low
web-resource languages (LRL). However, due to limited model capacity, the large
difference in the sizes of available monolingual corpora between high
web-resource languag... | Here's an insight: |
[
" Large Language Models (LLMs) are known to memorize significant portions of\ntheir training data. Parts of this memorized content have been shown to be\nextractable by simply querying the model, which poses a privacy risk. We\npresent a novel approach which uses prompt-tuning to control the extraction\nrates of m... | 2305.11759 | 2205.12854 | 2305.11759_2205.12854 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Large Language Models (LLMs) are known to memorize significant portions of
their training data. Parts of this memorized content have been shown to be
extractable by simply querying the model, which poses a privacy risk. We
present a novel approach which uses prompt-tuning to control the extraction
rates of mem... | Paper:
Large Language Models (LLMs) are known to memorize significant portions of
their training data. Parts of this memorized content have been shown to be
extractable by simply querying the model, which poses a privacy risk. We
present a novel approach which uses prompt-tuning to control the extraction
rates of memor... | Paper:
The propensity of abstractive summarization models to make factual errors has
been studied extensively, including design of metrics to detect factual errors
and annotation of errors in current systems' outputs. However, the
ever-evolving nature of summarization systems, metrics, and annotated
benchmarks makes fa... | Here's an insight: |
[
" State-sponsored trolls are the main actors of influence campaigns on social\nmedia and automatic troll detection is important to combat misinformation at\nscale. Existing troll detection models are developed based on training data for\nknown campaigns (e.g.\\ the influence campaign by Russia's Internet Research\... | 2303.07354 | 2212.13036 | 2303.07354_2212.13036 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
State-sponsored trolls are the main actors of influence campaigns on social
media and automatic troll detection is important to combat misinformation at
scale. Existing troll detection models are developed based on training data for
known campaigns (e.g.\ the influence campaign by Russia's Internet Research
Ag... | Paper:
State-sponsored trolls are the main actors of influence campaigns on social
media and automatic troll detection is important to combat misinformation at
scale. Existing troll detection models are developed based on training data for
known campaigns (e.g.\ the influence campaign by Russia's Internet Research
Agen... | Paper:
Complex knowledge base question answering can be achieved by converting
questions into sequences of predefined actions. However, there is a significant
semantic and structural gap between natural language and action sequences,
which makes this conversion difficult. In this paper, we introduce an
alignment-enhanc... | Here's an insight: |
[
" As text generated by large language models proliferates, it becomes vital to\nunderstand how humans engage with such text, and whether or not they are able\nto detect when the text they are reading did not originate with a human writer.\nPrior work on human detection of generated text focuses on the case where a... | 2212.12672 | 2106.14361 | 2212.12672_2106.14361 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
As text generated by large language models proliferates, it becomes vital to
understand how humans engage with such text, and whether or not they are able
to detect when the text they are reading did not originate with a human writer.
Prior work on human detection of generated text focuses on the case where an... | Paper:
As text generated by large language models proliferates, it becomes vital to
understand how humans engage with such text, and whether or not they are able
to detect when the text they are reading did not originate with a human writer.
Prior work on human detection of generated text focuses on the case where an
e... | Paper:
Learning representations of words in a continuous space is perhaps the most
fundamental task in NLP, however words interact in ways much richer than vector
dot product similarity can provide. Many relationships between words can be
expressed set-theoretically, for example, adjective-noun compounds (eg. "red
cars... | Here's an insight: |
[
" Despite tremendous progress in automatic summarization, state-of-the-art\nmethods are predominantly trained to excel in summarizing short newswire\narticles, or documents with strong layout biases such as scientific articles or\ngovernment reports. Efficient techniques to summarize financial documents,\nincludin... | 2210.12467 | 2110.13900 | 2210.12467_2110.13900 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Despite tremendous progress in automatic summarization, state-of-the-art
methods are predominantly trained to excel in summarizing short newswire
articles, or documents with strong layout biases such as scientific articles or
government reports. Efficient techniques to summarize financial documents,
including ... | Paper:
Despite tremendous progress in automatic summarization, state-of-the-art
methods are predominantly trained to excel in summarizing short newswire
articles, or documents with strong layout biases such as scientific articles or
government reports. Efficient techniques to summarize financial documents,
including fa... | Paper:
Self-supervised learning (SSL) achieves great success in speech recognition,
while limited exploration has been attempted for other speech processing tasks.
As speech signal contains multi-faceted information including speaker identity,
paralinguistics, spoken content, etc., learning universal representations fo... | Here's an insight: |
[
" This paper focuses on automatically generating the text of an ad, and the\ngoal is that the generated text can capture user interest for achieving higher\nclick-through rate (CTR). We propose CREATER, a CTR-driven advertising text\ngeneration approach, to generate ad texts based on high-quality user reviews.\nTo... | 2205.08943 | 2203.07627 | 2205.08943_2203.07627 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
This paper focuses on automatically generating the text of an ad, and the
goal is that the generated text can capture user interest for achieving higher
click-through rate (CTR). We propose CREATER, a CTR-driven advertising text
generation approach, to generate ad texts based on high-quality user reviews.
To i... | Paper:
This paper focuses on automatically generating the text of an ad, and the
goal is that the generated text can capture user interest for achieving higher
click-through rate (CTR). We propose CREATER, a CTR-driven advertising text
generation approach, to generate ad texts based on high-quality user reviews.
To inc... | Paper:
Multilingual neural machine translation models are trained to maximize the
likelihood of a mix of examples drawn from multiple language pairs. The
dominant inductive bias applied to these models is a shared vocabulary and a
shared set of parameters across languages; the inputs and labels corresponding
to example... | Here's an insight: |
[
" Hope is characterized as openness of spirit toward the future, a desire,\nexpectation, and wish for something to happen or to be true that remarkably\naffects human's state of mind, emotions, behaviors, and decisions. Hope is\nusually associated with concepts of desired expectations and\npossibility/probability ... | 2210.14136 | 2205.00034 | 2210.14136_2205.00034 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Hope is characterized as openness of spirit toward the future, a desire,
expectation, and wish for something to happen or to be true that remarkably
affects human's state of mind, emotions, behaviors, and decisions. Hope is
usually associated with concepts of desired expectations and
possibility/probability co... | Paper:
Hope is characterized as openness of spirit toward the future, a desire,
expectation, and wish for something to happen or to be true that remarkably
affects human's state of mind, emotions, behaviors, and decisions. Hope is
usually associated with concepts of desired expectations and
possibility/probability conc... | Paper:
Named Entity Recognition (NER) is a well researched NLP task and is widely
used in real world NLP scenarios. NER research typically focuses on the
creation of new ways of training NER, with relatively less emphasis on
resources and evaluation. Further, state of the art (SOTA) NER models, trained
on standard data... | Here's an insight: |
[
" Class-based language models (LMs) have been long devised to address context\nsparsity in $n$-gram LMs. In this study, we revisit this approach in the\ncontext of neural LMs. We hypothesize that class-based prediction leads to an\nimplicit context aggregation for similar words and thus can improve\ngeneralization... | 2203.10692 | 2202.07543 | 2203.10692_2202.07543 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Class-based language models (LMs) have been long devised to address context
sparsity in $n$-gram LMs. In this study, we revisit this approach in the
context of neural LMs. We hypothesize that class-based prediction leads to an
implicit context aggregation for similar words and thus can improve
generalization f... | Paper:
Class-based language models (LMs) have been long devised to address context
sparsity in $n$-gram LMs. In this study, we revisit this approach in the
context of neural LMs. We hypothesize that class-based prediction leads to an
implicit context aggregation for similar words and thus can improve
generalization for... | Paper:
Memes are prevalent on the internet and continue to grow and evolve alongside
our culture. An automatic understanding of memes propagating on the internet
can shed light on the general sentiment and cultural attitudes of people. In
this work, we present team BLUE's solution for the second edition of the
MEMOTION... | Here's an insight: |
[
" Automated event detection from news corpora is a crucial task towards mining\nfast-evolving structured knowledge. As real-world events have different\ngranularities, from the top-level themes to key events and then to event\nmentions corresponding to concrete actions, there are generally two lines of\nresearch: ... | 2206.04153 | 2212.06121 | 2206.04153_2212.06121 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Automated event detection from news corpora is a crucial task towards mining
fast-evolving structured knowledge. As real-world events have different
granularities, from the top-level themes to key events and then to event
mentions corresponding to concrete actions, there are generally two lines of
research: (1... | Paper:
Automated event detection from news corpora is a crucial task towards mining
fast-evolving structured knowledge. As real-world events have different
granularities, from the top-level themes to key events and then to event
mentions corresponding to concrete actions, there are generally two lines of
research: (1) ... | Paper:
Bi-encoders and cross-encoders are widely used in many state-of-the-art
retrieval pipelines. In this work we study the generalization ability of these
two types of architectures on a wide range of parameter count on both in-domain
and out-of-domain scenarios. We find that the number of parameters and early
query... | Here's an insight: |
[
" The transducer architecture is becoming increasingly popular in the field of\nspeech recognition, because it is naturally streaming as well as high in\naccuracy. One of the drawbacks of transducer is that it is difficult to decode\nin a fast and parallel way due to an unconstrained number of symbols that can\nbe... | 2211.00484 | 2205.02564 | 2211.00484_2205.02564 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
The transducer architecture is becoming increasingly popular in the field of
speech recognition, because it is naturally streaming as well as high in
accuracy. One of the drawbacks of transducer is that it is difficult to decode
in a fast and parallel way due to an unconstrained number of symbols that can
be e... | Paper:
The transducer architecture is becoming increasingly popular in the field of
speech recognition, because it is naturally streaming as well as high in
accuracy. One of the drawbacks of transducer is that it is difficult to decode
in a fast and parallel way due to an unconstrained number of symbols that can
be emi... | Paper:
Complex Word Identification (CWI) aims to detect words within a text that a
reader may find difficult to understand. It has been shown that CWI systems can
improve text simplification, readability prediction and vocabulary acquisition
modelling. However, the difficulty of a word is a highly idiosyncratic notion
... | Here's an insight: |
[
" Robustness evaluation against adversarial examples has become increasingly\nimportant to unveil the trustworthiness of the prevailing deep models in\nnatural language processing (NLP). However, in contrast to the computer vision\ndomain where the first-order projected gradient descent (PGD) is used as the\nbench... | 2212.09254 | 2205.14140 | 2212.09254_2205.14140 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Robustness evaluation against adversarial examples has become increasingly
important to unveil the trustworthiness of the prevailing deep models in
natural language processing (NLP). However, in contrast to the computer vision
domain where the first-order projected gradient descent (PGD) is used as the
benchma... | Paper:
Robustness evaluation against adversarial examples has become increasingly
important to unveil the trustworthiness of the prevailing deep models in
natural language processing (NLP). However, in contrast to the computer vision
domain where the first-order projected gradient descent (PGD) is used as the
benchmark... | Paper:
The increasing size and complexity of modern ML systems has improved their
predictive capabilities but made their behavior harder to explain. Many
techniques for model explanation have been developed in response, but we lack
clear criteria for assessing these techniques. In this paper, we cast model
explanation ... | Here's an insight: |
[
" Sustaining coherent and engaging narratives requires dialogue or storytelling\nagents to understand how the personas of speakers or listeners ground the\nnarrative. Specifically, these agents must infer personas of their listeners to\nproduce statements that cater to their interests. They must also learn to\nmai... | 2305.02364 | 2208.12995 | 2305.02364_2208.12995 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
Sustaining coherent and engaging narratives requires dialogue or storytelling
agents to understand how the personas of speakers or listeners ground the
narrative. Specifically, these agents must infer personas of their listeners to
produce statements that cater to their interests. They must also learn to
maint... | Paper:
Sustaining coherent and engaging narratives requires dialogue or storytelling
agents to understand how the personas of speakers or listeners ground the
narrative. Specifically, these agents must infer personas of their listeners to
produce statements that cater to their interests. They must also learn to
maintai... | Paper:
Successful Machine Learning based Named Entity Recognition models could fail
on texts from some special domains, for instance, Chinese addresses and
e-commerce titles, where requires adequate background knowledge. Such texts are
also difficult for human annotators. In fact, we can obtain some potentially
helpful... | Here's an insight: |
[
" We apply transfer learning to the task of phoneme segmentation and\ndemonstrate the utility of representations learned in self-supervised\npre-training for the task. Our model extends transformer-style encoders with\nstrategically placed convolutions that manipulate features learned in\npre-training. Using the T... | 2211.01461 | 2202.05451 | 2211.01461_2202.05451 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | Paper 1:
We apply transfer learning to the task of phoneme segmentation and
demonstrate the utility of representations learned in self-supervised
pre-training for the task. Our model extends transformer-style encoders with
strategically placed convolutions that manipulate features learned in
pre-training. Using the TIM... | Paper:
We apply transfer learning to the task of phoneme segmentation and
demonstrate the utility of representations learned in self-supervised
pre-training for the task. Our model extends transformer-style encoders with
strategically placed convolutions that manipulate features learned in
pre-training. Using the TIMIT... | Paper:
Recent research that applies Transformer-based architectures to image
captioning has resulted in state-of-the-art image captioning performance,
capitalising on the success of Transformers on natural language tasks.
Unfortunately, though these models work well, one major flaw is their large
model sizes. To this e... | Here's an insight: |
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