id_1 stringlengths 9 14 | abstract_1 stringlengths 296 1.92k | authors_1 stringlengths 6 1.32k | title_1 stringlengths 12 159 | journal-ref_1 stringclasses 110
values | categories_1 stringclasses 154
values | update_date_1 timestamp[ns] | id_2 stringlengths 9 14 | abstract_2 stringlengths 296 1.92k | authors_2 stringlengths 6 1.32k | title_2 stringlengths 12 159 | journal-ref_2 stringclasses 110
values | categories_2 stringclasses 154
values | update_date_2 timestamp[ns] |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2312.11511 | We present ComplexityNet, a streamlined language model designed for assessing
task complexity. This model predicts the likelihood of accurate output by
various language models, each with different capabilities. Our initial
application of ComplexityNet involves the Mostly Basic Python Problems (MBPP)
dataset. We pione... | Henry Bae, Aghyad Deeb, Alex Fleury, Kehang Zhu | ComplexityNet: Increasing LLM Inference Efficiency by Learning Task
Complexity | null | cs.CL cs.AI cs.LG | 2024-10-16T00:00:00 | 2502.13647 | Instruction tuning in low-resource languages remains underexplored due to
limited text data, particularly in government and cultural domains. To address
this, we introduce and open-source a large-scale (10,600 samples)
instruction-following (IFT) dataset, covering key institutional and cultural
knowledge relevant to ... | Nurkhan Laiyk, Daniil Orel, Rituraj Joshi, Maiya Goloburda, Yuxia
Wang, Preslav Nakov, Fajri Koto | Instruction Tuning on Public Government and Cultural Data for
Low-Resource Language: a Case Study in Kazakh | null | cs.CL | 2025-02-20T00:00:00 |
2309.02427 | Recent efforts have augmented large language models (LLMs) with external
resources (e.g., the Internet) or internal control flows (e.g., prompt
chaining) for tasks requiring grounding or reasoning, leading to a new class of
language agents. While these agents have achieved substantial empirical
success, we lack a sys... | Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L.
Griffiths | Cognitive Architectures for Language Agents | null | cs.AI cs.CL cs.LG cs.SC | 2024-03-18T00:00:00 | 1804.09552 | Transcribing voice communications in NASA's launch control center is
important for information utilization. However, automatic speech recognition in
this environment is particularly challenging due to the lack of training data,
unfamiliar words in acronyms, multiple different speakers and accents, and
conversational ... | Kyongsik Yun, Joseph Osborne, Madison Lee, Thomas Lu, Edward Chow | Automatic speech recognition for launch control center communication
using recurrent neural networks with data augmentation and custom language
model | null | cs.CL cs.HC | 2018-04-26T00:00:00 |
1206.6423 | As robots become more ubiquitous and capable, it becomes ever more important
to enable untrained users to easily interact with them. Recently, this has led
to study of the language grounding problem, where the goal is to extract
representations of the meanings of natural language tied to perception and
actuation in t... | Cynthia Matuszek (University of Washington), Nicholas FitzGerald
(University of Washington), Luke Zettlemoyer (University of Washington),
Liefeng Bo (University of Washington), Dieter Fox (University of Washington) | A Joint Model of Language and Perception for Grounded Attribute Learning | null | cs.CL cs.LG cs.RO | 2012-07-03T00:00:00 | cmp-lg/9703001 | In this paper, a method of domain adaptation for clustered language models is
developed. It is based on a previously developed clustering algorithm, but with
a modified optimisation criterion. The results are shown to be slightly
superior to the previously published 'Fillup' method, which can be used to
adapt standar... | Joerg P. Ueberla (Forum Technology - DRA Malvern) | Domain Adaptation with Clustered Language Models | null | cmp-lg cs.CL | 2008-02-03T00:00:00 |
1911.03353 | We introduce a new scientific named entity recognizer called SEPT, which
stands for Span Extractor with Pre-trained Transformers. In recent papers, span
extractors have been demonstrated to be a powerful model compared with sequence
labeling models. However, we discover that with the development of pre-trained
langua... | Tan Yan, Heyan Huang, Xian-Ling Mao | SEPT: Improving Scientific Named Entity Recognition with Span
Representation | null | cs.CL cs.IR | 2020-10-14T00:00:00 | 2010.01063 | Neural networks trained on natural language processing tasks capture syntax
even though it is not provided as a supervision signal. This indicates that
syntactic analysis is essential to the understating of language in artificial
intelligence systems. This overview paper covers approaches of evaluating the
amount of ... | Tomasz Limisiewicz and David Mare\v{c}ek | Syntax Representation in Word Embeddings and Neural Networks -- A Survey | Proceedings of the 20th Conference ITAT 2020: Automata, Formal and
Natural Languages Workshop | cs.CL | 2020-10-05T00:00:00 |
1806.09055 | This paper addresses the scalability challenge of architecture search by
formulating the task in a differentiable manner. Unlike conventional approaches
of applying evolution or reinforcement learning over a discrete and
non-differentiable search space, our method is based on the continuous
relaxation of the architec... | Hanxiao Liu, Karen Simonyan, Yiming Yang | DARTS: Differentiable Architecture Search | null | cs.LG cs.CL cs.CV stat.ML | 2019-04-24T00:00:00 | 1401.2258 | This work compares concept models for cross-language retrieval: First, we
adapt probabilistic Latent Semantic Analysis (pLSA) for multilingual documents.
Experiments with different weighting schemes show that a weighting method
favoring documents of similar length in both language sides gives best results.
Considerin... | Benjamin Roth | Assessing Wikipedia-Based Cross-Language Retrieval Models | null | cs.IR cs.CL | 2014-01-13T00:00:00 |
2202.13047 | Crowdsourced dialogue corpora are usually limited in scale and topic coverage
due to the expensive cost of data curation. This would hinder the
generalization of downstream dialogue models to open-domain topics. In this
work, we leverage large language models for dialogue augmentation in the task
of emotional support... | Chujie Zheng, Sahand Sabour, Jiaxin Wen, Zheng Zhang, Minlie Huang | AugESC: Dialogue Augmentation with Large Language Models for Emotional
Support Conversation | null | cs.CL | 2023-05-19T00:00:00 | 2308.06039 | In learning to defer, a predictor identifies risky decisions and defers them
to a human expert. One key issue with this setup is that the expert may end up
over-relying on the machine's decisions, due to anchoring bias. At the same
time, whenever the machine chooses the deferral option the expert has to take
decision... | Debodeep Banerjee, Stefano Teso, Andrea Passerini | Learning to Guide Human Experts via Personalized Large Language Models | null | cs.AI cs.CL | 2023-08-14T00:00:00 |
1412.8419 | Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We ... | Remi Lebret and Pedro O. Pinheiro and Ronan Collobert | Simple Image Description Generator via a Linear Phrase-Based Approach | null | cs.CL cs.CV cs.NE | 2015-04-14T00:00:00 | 2311.09210 | Retrieval-augmented language models (RALMs) represent a substantial
advancement in the capabilities of large language models, notably in reducing
factual hallucination by leveraging external knowledge sources. However, the
reliability of the retrieved information is not always guaranteed. The
retrieval of irrelevant ... | Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, Dong
Yu | Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language
Models | null | cs.CL cs.AI | 2024-10-04T00:00:00 |
2405.01159 | This paper presents the TartuNLP team submission to EvaLatin 2024 shared task
of the emotion polarity detection for historical Latin texts. Our system relies
on two distinct approaches to annotating training data for supervised learning:
1) creating heuristics-based labels by adopting the polarity lexicon provided
by... | Aleksei Dorkin and Kairit Sirts | TartuNLP at EvaLatin 2024: Emotion Polarity Detection | Proceedings of the Third Workshop on Language Technologies for
Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024 | cs.CL | 2024-12-10T00:00:00 | 2405.06424 | Assessing response quality to instructions in language models is vital but
challenging due to the complexity of human language across different contexts.
This complexity often results in ambiguous or inconsistent interpretations,
making accurate assessment difficult. To address this issue, we propose a novel
Uncertai... | JoonHo Lee, Jae Oh Woo, Juree Seok, Parisa Hassanzadeh, Wooseok Jang,
JuYoun Son, Sima Didari, Baruch Gutow, Heng Hao, Hankyu Moon, Wenjun Hu,
Yeong-Dae Kwon, Taehee Lee and Seungjai Min | Improving Instruction Following in Language Models through Proxy-Based
Uncertainty Estimation | null | cs.CL cs.AI cs.LG | 2025-02-03T00:00:00 |
1806.09055 | This paper addresses the scalability challenge of architecture search by
formulating the task in a differentiable manner. Unlike conventional approaches
of applying evolution or reinforcement learning over a discrete and
non-differentiable search space, our method is based on the continuous
relaxation of the architec... | Hanxiao Liu, Karen Simonyan, Yiming Yang | DARTS: Differentiable Architecture Search | null | cs.LG cs.CL cs.CV stat.ML | 2019-04-24T00:00:00 | 1807.03583 | Smoothing is an essential tool in many NLP tasks, therefore numerous
techniques have been developed for this purpose in the past. One of the most
widely used smoothing methods are the Kneser-Ney smoothing (KNS) and its
variants, including the Modified Kneser-Ney smoothing (MKNS), which are widely
considered to be amo... | Andr\'as Dob\'o | Multi-D Kneser-Ney Smoothing Preserving the Original Marginal
Distributions | Research in Computing Science, 147 (6), 11-25 | cs.CL | 2019-02-08T00:00:00 |
2111.07180 | In natural language processing, most models try to learn semantic
representations merely from texts. The learned representations encode the
distributional semantics but fail to connect to any knowledge about the
physical world. In contrast, humans learn language by grounding concepts in
perception and action and the ... | Yizhen Zhang, Minkyu Choi, Kuan Han, Zhongming Liu | Explainable Semantic Space by Grounding Language to Vision with
Cross-Modal Contrastive Learning | null | cs.CL cs.LG | 2021-11-16T00:00:00 | cmp-lg/9612005 | The Maximum Entropy Modeling Toolkit supports parameter estimation and
prediction for statistical language models in the maximum entropy framework.
The maximum entropy framework provides a constructive method for obtaining the
unique conditional distribution p*(y|x) that satisfies a set of linear
constraints and maxi... | Eric Sven Ristad | Maximum Entropy Modeling Toolkit | null | cmp-lg cs.CL | 2008-02-03T00:00:00 |
1705.01346 | Recurrent Neural Network (RNN) has been widely applied for sequence modeling.
In RNN, the hidden states at current step are full connected to those at
previous step, thus the influence from less related features at previous step
may potentially decrease model's learning ability. We propose a simple
technique called p... | Danhao Zhu, Si Shen, Xin-Yu Dai and Jiajun Chen | Going Wider: Recurrent Neural Network With Parallel Cells | null | cs.CL cs.LG cs.NE | 2017-05-04T00:00:00 | 1810.12387 | Most language modeling methods rely on large-scale data to statistically
learn the sequential patterns of words. In this paper, we argue that words are
atomic language units but not necessarily atomic semantic units. Inspired by
HowNet, we use sememes, the minimum semantic units in human languages, to
represent the i... | Yihong Gu, Jun Yan, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun,
Fen Lin, Leyu Lin | Language Modeling with Sparse Product of Sememe Experts | null | cs.CL cs.LG | 2018-10-31T00:00:00 |
1911.03829 | Large-scale pre-trained language model such as BERT has achieved great
success in language understanding tasks. However, it remains an open question
how to utilize BERT for language generation. In this paper, we present a novel
approach, Conditional Masked Language Modeling (C-MLM), to enable the
finetuning of BERT o... | Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, Jingjing Liu | Distilling Knowledge Learned in BERT for Text Generation | null | cs.CL cs.LG | 2020-07-21T00:00:00 | 1504.01182 | Machine dialect interpretation assumes a real part in encouraging man-machine
correspondence and in addition men-men correspondence in Natural Language
Processing (NLP). Machine Translation (MT) alludes to utilizing machine to
change one dialect to an alternate. Statistical Machine Translation is a type
of MT consist... | Nayan Jyoti Kalita, Baharul Islam | Bengali to Assamese Statistical Machine Translation using Moses (Corpus
Based) | null | cs.CL | 2015-04-07T00:00:00 |
1502.00512 | This paper investigates the scaling properties of Recurrent Neural Network
Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and
address the questions of how RNNLMs scale with respect to model size,
training-set size, computational costs and memory. Our analysis shows that
despite being more c... | Will Williams, Niranjani Prasad, David Mrva, Tom Ash, Tony Robinson | Scaling Recurrent Neural Network Language Models | null | cs.CL cs.LG | 2015-02-03T00:00:00 | 2102.07396 | We explore cross-lingual transfer of register classification for web
documents. Registers, that is, text varieties such as blogs or news are one of
the primary predictors of linguistic variation and thus affect the automatic
processing of language. We introduce two new register annotated corpora,
FreCORE and SweCORE,... | Liina Repo, Valtteri Skantsi, Samuel R\"onnqvist, Saara Hellstr\"om,
Miika Oinonen, Anna Salmela, Douglas Biber, Jesse Egbert, Sampo Pyysalo and
Veronika Laippala | Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight
Monolingual Classification of Registers | null | cs.CL | 2021-02-16T00:00:00 |
2006.10964 | COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has
caused more than 7.4 million cases and over 418,000 deaths. The highly dynamic
and rapidly evolving situation with COVID-19 has made it difficult to access
accurate, on-demand information regarding the disease. Online communities,
forums, and so... | David Oniani, Yanshan Wang | A Qualitative Evaluation of Language Models on Automatic
Question-Answering for COVID-19 | null | cs.IR cs.AI cs.CL | 2020-06-25T00:00:00 | 1904.10641 | Machine-translated text plays an important role in modern life by smoothing
communication from various communities using different languages. However,
unnatural translation may lead to misunderstanding, a detector is thus needed
to avoid the unfortunate mistakes. While a previous method measured the
naturalness of co... | Hoang-Quoc Nguyen-Son and Tran Phuong Thao and Seira Hidano and
Shinsaku Kiyomoto | Detecting Machine-Translated Paragraphs by Matching Similar Words | null | cs.CL | 2019-04-25T00:00:00 |
1807.09433 | Recent advances in statistical machine translation via the adoption of neural
sequence-to-sequence models empower the end-to-end system to achieve
state-of-the-art in many WMT benchmarks. The performance of such machine
translation (MT) system is usually evaluated by automatic metric BLEU when the
golden references a... | Kai Fan, Jiayi Wang, Bo Li, Fengming Zhou, Boxing Chen, Luo Si | "Bilingual Expert" Can Find Translation Errors | null | cs.CL | 2018-11-20T00:00:00 | 2105.10419 | Existing models of multilingual sentence embeddings require large parallel
data resources which are not available for low-resource languages. We propose a
novel unsupervised method to derive multilingual sentence embeddings relying
only on monolingual data. We first produce a synthetic parallel corpus using
unsupervi... | Ivana Kvapil{\i}kova, Mikel Artetxe, Gorka Labaka, Eneko Agirre,
Ond\v{r}ej Bojar | Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining | Proceedings of the 58th Annual Meeting of the Association for
Computational Linguistics - Student Research Workshop, pages 255-262,
Association for Computational Linguistics, 2020 | cs.CL | 2021-05-24T00:00:00 |
cs/9912016 | We present a technique which complements Hidden Markov Models by
incorporating some lexicalized states representing syntactically uncommon
words. Our approach examines the distribution of transitions, selects the
uncommon words, and makes lexicalized states for the words. We performed a
part-of-speech tagging experim... | Jin-Dong Kim and Sang-Zoo Lee and Hae-Chang Rim | HMM Specialization with Selective Lexicalization | Proceedings of the 1999 Joint SIGDAT Conference on Empirical
Methods in Natural Language Processing and Very Large Corpora, pp.121-127,
1999 | cs.CL cs.LG | 2007-05-23T00:00:00 | 2008.07267 | Natural language processing (NLP) and neural networks (NNs) have both
undergone significant changes in recent years. For active learning (AL)
purposes, NNs are, however, less commonly used -- despite their current
popularity. By using the superior text classification performance of NNs for
AL, we can either increase ... | Christopher Schr\"oder and Andreas Niekler | A Survey of Active Learning for Text Classification using Deep Neural
Networks | null | cs.CL cs.LG | 2020-08-18T00:00:00 |
1704.08352 | Words can be represented by composing the representations of subword units
such as word segments, characters, and/or character n-grams. While such
representations are effective and may capture the morphological regularities of
words, they have not been systematically compared, and it is not understood how
they intera... | Clara Vania and Adam Lopez | From Characters to Words to in Between: Do We Capture Morphology? | null | cs.CL | 2017-04-28T00:00:00 | 1902.09969 | Inspired by recent advances in leveraging multiple modalities in machine
translation, we introduce an encoder-decoder pipeline that uses (1) specific
objects within an image and their object labels, (2) a language model for
decoding joint embedding of object features and the object labels. Our pipeline
merges prior d... | Ashutosh Mishra, Marcus Liwicki | Using Deep Object Features for Image Descriptions | null | cs.CV cs.CL cs.LG | 2019-02-27T00:00:00 |
2403.02615 | We present a comprehensive evaluation of large language models(LLMs)' ability
to reason about composition relations through a benchmark encompassing 1,500
test cases in English, designed to cover six distinct types of composition
relations: Positional, Comparative, Personal, Mathematical, Identity, and
Other. Acknowl... | Jinman Zhao, Xueyan Zhang | Exploring the Limitations of Large Language Models in Compositional
Relation Reasoning | null | cs.CL | 2024-09-24T00:00:00 | 1905.13150 | In the broadcast domain there is an abundance of related text data and
partial transcriptions, such as closed captions and subtitles. This text data
can be used for lightly supervised training, in which text matching the audio
is selected using an existing speech recognition model. Current approaches to
light supervi... | Joachim Fainberg, Ond\v{r}ej Klejch, Steve Renals, Peter Bell | Lattice-based lightly-supervised acoustic model training | null | cs.CL cs.SD eess.AS | 2019-07-16T00:00:00 |
cs/0108006 | Maximum entropy models are considered by many to be one of the most promising
avenues of language modeling research. Unfortunately, long training times make
maximum entropy research difficult. We present a novel speedup technique: we
change the form of the model to use classes. Our speedup works by creating two
maxim... | Joshua Goodman | Classes for Fast Maximum Entropy Training | Proceedings of ICASSP-2001, Utah, May 2001 | cs.CL | 2007-05-23T00:00:00 | 2410.18963 | Large language models (LLMs) and large multimodal models (LMMs) have shown
great potential in automating complex tasks like web browsing and gaming.
However, their ability to generalize across diverse applications remains
limited, hindering broader utility. To address this challenge, we present
OSCAR: Operating Syste... | Xiaoqiang Wang and Bang Liu | OSCAR: Operating System Control via State-Aware Reasoning and
Re-Planning | null | cs.AI cs.CL | 2024-10-25T00:00:00 |
2008.08547 | Pre-trained language model word representation, such as BERT, have been
extremely successful in several Natural Language Processing tasks significantly
improving on the state-of-the-art. This can largely be attributed to their
ability to better capture semantic information contained within a sentence.
Several tasks, ... | Wah Meng Lim and Harish Tayyar Madabushi | UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information | null | cs.CL | 2020-08-20T00:00:00 | 2103.07052 | We propose an unsupervised solution to the Authorship Verification task that
utilizes pre-trained deep language models to compute a new metric called
DV-Distance. The proposed metric is a measure of the difference between the two
authors comparing against pre-trained language models. Our design addresses the
problem ... | Yifan Zhang, Dainis Boumber, Marjan Hosseinia, Fan Yang, Arjun
Mukherjee | Improving Authorship Verification using Linguistic Divergence | null | cs.CL cs.IR cs.LG | 2021-03-15T00:00:00 |
2311.09358 | Large language models (LLMs) have shown remarkable achievements in natural
language processing tasks, producing high-quality outputs. However, LLMs still
exhibit limitations, including the generation of factually incorrect
information. In safety-critical applications, it is important to assess the
confidence of LLM-g... | Sridevi Wagle, Sai Munikoti, Anurag Acharya, Sara Smith, Sameera
Horawalavithana | Empirical evaluation of Uncertainty Quantification in
Retrieval-Augmented Language Models for Science | null | cs.CL cs.AI | 2023-11-17T00:00:00 | 1904.04697 | Chinese word segmentation and dependency parsing are two fundamental tasks
for Chinese natural language processing. The dependency parsing is defined on
word-level. Therefore word segmentation is the precondition of dependency
parsing, which makes dependency parsing suffer from error propagation and
unable to directl... | Hang Yan, Xipeng Qiu, Xuanjing Huang | A Graph-based Model for Joint Chinese Word Segmentation and Dependency
Parsing | null | cs.CL cs.AI | 2019-12-19T00:00:00 |
2203.11199 | Recently, the problem of robustness of pre-trained language models (PrLMs)
has received increasing research interest. Latest studies on adversarial
attacks achieve high attack success rates against PrLMs, claiming that PrLMs
are not robust. However, we find that the adversarial samples that PrLMs fail
are mostly non-... | Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao | Distinguishing Non-natural from Natural Adversarial Samples for More
Robust Pre-trained Language Model | null | cs.LG cs.CL cs.CR | 2022-03-23T00:00:00 | 2305.13707 | Language models have graduated from being research prototypes to
commercialized products offered as web APIs, and recent works have highlighted
the multilingual capabilities of these products. The API vendors charge their
users based on usage, more specifically on the number of ``tokens'' processed
or generated by th... | Orevaoghene Ahia, Sachin Kumar, Hila Gonen, Jungo Kasai, David R.
Mortensen, Noah A. Smith, Yulia Tsvetkov | Do All Languages Cost the Same? Tokenization in the Era of Commercial
Language Models | null | cs.CL | 2023-05-24T00:00:00 |
2306.08000 | Recent advances in zero-shot learning have enabled the use of paired
image-text data to replace structured labels, replacing the need for expert
annotated datasets. Models such as CLIP-based CheXzero utilize these
advancements in the domain of chest X-ray interpretation. We hypothesize that
domain pre-trained models ... | Aakash Mishra, Rajat Mittal, Christy Jestin, Kostas Tingos, Pranav
Rajpurkar | Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using
Domain Pre-trained Language Models | null | physics.med-ph cs.CL cs.CV cs.LG eess.IV | 2023-06-16T00:00:00 | 2201.11147 | Self-supervised protein language models have proved their effectiveness in
learning the proteins representations. With the increasing computational power,
current protein language models pre-trained with millions of diverse sequences
can advance the parameter scale from million-level to billion-level and achieve
rema... | Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong,
Shumin Deng, Jiazhang Lian, Qiang Zhang, Huajun Chen | OntoProtein: Protein Pretraining With Gene Ontology Embedding | null | q-bio.BM cs.AI cs.CL cs.IR cs.LG | 2022-11-02T00:00:00 |
2305.12710 | Real-world domain experts (e.g., doctors) rarely annotate only a decision
label in their day-to-day workflow without providing explanations. Yet,
existing low-resource learning techniques, such as Active Learning (AL), that
aim to support human annotators mostly focus on the label while neglecting the
natural languag... | Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh,
Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo
Wang | Beyond Labels: Empowering Human Annotators with Natural Language
Explanations through a Novel Active-Learning Architecture | null | cs.CL | 2023-10-24T00:00:00 | 2009.04016 | This paper describes Brown University's submission to the TREC 2019 Deep
Learning track. We followed a 2-phase method for producing a ranking of
passages for a given input query: In the the first phase, the user's query is
expanded by appending 3 queries generated by a transformer model which was
trained to rephrase ... | George Zerveas, Ruochen Zhang, Leila Kim, Carsten Eickhoff | Brown University at TREC Deep Learning 2019 | Proceedings of the Twenty-Eighth Text REtrieval Conference, TREC
2019, Gaithersburg, Maryland, USA, November 13-15, 2019. NIST Special
Publication 1250, National Institute of Standards and Technology (NIST) 2019 | cs.IR cs.CL cs.LG | 2020-09-10T00:00:00 |
1708.00077 | Recurrent neural networks show state-of-the-art results in many text analysis
tasks but often require a lot of memory to store their weights. Recently
proposed Sparse Variational Dropout eliminates the majority of the weights in a
feed-forward neural network without significant loss of quality. We apply this
techniqu... | Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov | Bayesian Sparsification of Recurrent Neural Networks | null | stat.ML cs.CL cs.LG | 2017-08-02T00:00:00 | 1703.09137 | When a recurrent neural network language model is used for caption
generation, the image information can be fed to the neural network either by
directly incorporating it in the RNN -- conditioning the language model by
`injecting' image features -- or in a layer following the RNN -- conditioning
the language model by... | Marc Tanti (1), Albert Gatt (1), Kenneth P. Camilleri (1) ((1)
University of Malta) | Where to put the Image in an Image Caption Generator | null | cs.NE cs.CL cs.CV | 2018-03-15T00:00:00 |
1508.02091 | We examine the possibility that recent promising results in automatic caption
generation are due primarily to language models. By varying image
representation quality produced by a convolutional neural network, we find that
a state-of-the-art neural captioning algorithm is able to produce quality
captions even when p... | Jack Hessel, Nicolas Savva, Michael J. Wilber | Image Representations and New Domains in Neural Image Captioning | null | cs.CL cs.CV | 2015-08-11T00:00:00 | 2502.16761 | Large language models (LLMs) present novel opportunities in public opinion
research by predicting survey responses in advance during the early stages of
survey design. Prior methods steer LLMs via descriptions of subpopulations as
LLMs' input prompt, yet such prompt engineering approaches have struggled to
faithfully... | Joseph Suh, Erfan Jahanparast, Suhong Moon, Minwoo Kang, Serina Chang | Language Model Fine-Tuning on Scaled Survey Data for Predicting
Distributions of Public Opinions | null | cs.CL | 2025-02-25T00:00:00 |
cs/0105016 | This paper describes the functioning of a broad-coverage probabilistic
top-down parser, and its application to the problem of language modeling for
speech recognition. The paper first introduces key notions in language modeling
and probabilistic parsing, and briefly reviews some previous approaches to
using syntactic... | Brian Roark | Probabilistic top-down parsing and language modeling | null | cs.CL | 2007-05-23T00:00:00 | 1912.00159 | This paper presents SwissCrawl, the largest Swiss German text corpus to date.
Composed of more than half a million sentences, it was generated using a
customized web scraping tool that could be applied to other low-resource
languages as well. The approach demonstrates how freely available web pages can
be used to con... | Lucy Linder, Michael Jungo, Jean Hennebert, Claudiu Musat, Andreas
Fischer | Automatic Creation of Text Corpora for Low-Resource Languages from the
Internet: The Case of Swiss German | Proceedings of The 12th Language Resources and Evaluation
Conference, LREC (2020) 2706-2711 | cs.CL | 2020-06-17T00:00:00 |
2010.11428 | For various speech-related tasks, confidence scores from a speech recogniser
are a useful measure to assess the quality of transcriptions. In traditional
hidden Markov model-based automatic speech recognition (ASR) systems,
confidence scores can be reliably obtained from word posteriors in decoding
lattices. However,... | Qiujia Li, David Qiu, Yu Zhang, Bo Li, Yanzhang He, Philip C.
Woodland, Liangliang Cao, Trevor Strohman | Confidence Estimation for Attention-based Sequence-to-sequence Models
for Speech Recognition | null | eess.AS cs.CL cs.LG | 2020-10-27T00:00:00 | 2310.01041 | Despite the remarkable advances in language modeling, current mainstream
decoding methods still struggle to generate texts that align with human texts
across different aspects. In particular, sampling-based methods produce
less-repetitive texts which are often disjunctive in discourse, while
search-based methods main... | Haozhe Ji, Pei Ke, Hongning Wang, Minlie Huang | Language Model Decoding as Direct Metrics Optimization | The Twelfth International Conference on Learning Representations
(ICLR 2024) | cs.CL | 2024-06-06T00:00:00 |
2202.12226 | Sampling is a promising bottom-up method for exposing what generative models
have learned about language, but it remains unclear how to generate
representative samples from popular masked language models (MLMs) like BERT.
The MLM objective yields a dependency network with no guarantee of consistent
conditional distri... | Takateru Yamakoshi, Thomas L. Griffiths, Robert D. Hawkins | Probing BERT's priors with serial reproduction chains | null | cs.CL | 2022-03-21T00:00:00 | 1902.06000 | Semantic parsing using hierarchical representations has recently been
proposed for task oriented dialog with promising results [Gupta et al 2018]. In
this paper, we present three different improvements to the model:
contextualized embeddings, ensembling, and pairwise re-ranking based on a
language model. We taxonomiz... | Arash Einolghozati, Panupong Pasupat, Sonal Gupta, Rushin Shah, Mrinal
Mohit, Mike Lewis, Luke Zettlemoyer | Improving Semantic Parsing for Task Oriented Dialog | null | cs.CL cs.AI | 2019-02-19T00:00:00 |
2406.02378 | Large Language Models (LLMs) are able to improve their responses when
instructed to do so, a capability known as self-correction. When instructions
provide only the task's goal without specific details about potential issues in
the response, LLMs must rely on their internal knowledge to improve response
quality, a pr... | Guangliang Liu, Haitao Mao, Bochuan Cao, Zhiyu Xue, Xitong Zhang,
Rongrong Wang, Jiliang Tang, Kristen Johnson | On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and
Latent Concept | null | cs.CL | 2024-11-11T00:00:00 | 1602.07393 | Authorship attribution refers to the task of automatically determining the
author based on a given sample of text. It is a problem with a long history and
has a wide range of application. Building author profiles using language models
is one of the most successful methods to automate this task. New language
modeling ... | Zhenhao Ge and Yufang Sun | Domain Specific Author Attribution Based on Feedforward Neural Network
Language Models | null | cs.CL cs.LG cs.NE | 2016-02-25T00:00:00 |
1909.02560 | Revealing the robustness issues of natural language processing models and
improving their robustness is important to their performance under difficult
situations. In this paper, we study the robustness of paraphrase identification
models from a new perspective -- via modification with shared words, and we
show that t... | Zhouxing Shi, Minlie Huang | Robustness to Modification with Shared Words in Paraphrase
Identification | null | cs.CL | 2020-10-06T00:00:00 | 1908.05731 | Previous work on neural noisy channel modeling relied on latent variable
models that incrementally process the source and target sentence. This makes
decoding decisions based on partial source prefixes even though the full source
is available. We pursue an alternative approach based on standard sequence to
sequence m... | Kyra Yee and Nathan Ng and Yann N. Dauphin and Michael Auli | Simple and Effective Noisy Channel Modeling for Neural Machine
Translation | null | cs.CL | 2019-08-19T00:00:00 |
1904.03651 | Neural sequence-to-sequence models are currently the dominant approach in
several natural language processing tasks, but require large parallel corpora.
We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting
of two chained encoder-decoder pairs, with words used as a sequence of discrete
latent ... | Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas, Alexandros
Potamianos | SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for
Unsupervised Abstractive Sentence Compression | null | cs.CL | 2019-06-11T00:00:00 | 2109.04867 | As neural language models approach human performance on NLP benchmark tasks,
their advances are widely seen as evidence of an increasingly complex
understanding of syntax. This view rests upon a hypothesis that has not yet
been empirically tested: that word order encodes meaning essential to
performing these tasks. W... | Nikolay Malkin, Sameera Lanka, Pranav Goel, Nebojsa Jojic | Studying word order through iterative shuffling | null | cs.CL | 2021-09-13T00:00:00 |
1909.08582 | Training code-switched language models is difficult due to lack of data and
complexity in the grammatical structure. Linguistic constraint theories have
been used for decades to generate artificial code-switching sentences to cope
with this issue. However, this require external word alignments or constituency
parsers... | Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, Pascale Fung | Code-Switched Language Models Using Neural Based Synthetic Data from
Parallel Sentences | null | cs.CL | 2019-09-19T00:00:00 | 1611.01702 | In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based
language model designed to directly capture the global semantic meaning
relating words in a document via latent topics. Because of their sequential
nature, RNNs are good at capturing the local structure of a word sequence -
both semantic and s... | Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley | TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency | null | cs.CL cs.AI cs.LG stat.ML | 2017-02-28T00:00:00 |
cmp-lg/9502029 | This paper proposes a corpus-based language model for topic identification.
We analyze the association of noun-noun and noun-verb pairs in LOB Corpus. The
word association norms are based on three factors: 1) word importance, 2) pair
co-occurrence, and 3) distance. They are trained on the paragraph and sentence
level... | Kuang-hua Chen (Department of Computer Science and Information
Engineering, National Taiwan University) | Topic Identification in Discourse | null | cmp-lg cs.CL | 2016-08-31T00:00:00 | 1609.03777 | Recurrent neural network (RNN) based character-level language models (CLMs)
are extremely useful for modeling out-of-vocabulary words by nature. However,
their performance is generally much worse than the word-level language models
(WLMs), since CLMs need to consider longer history of tokens to properly
predict the n... | Kyuyeon Hwang, Wonyong Sung | Character-Level Language Modeling with Hierarchical Recurrent Neural
Networks | null | cs.LG cs.CL cs.NE | 2017-02-03T00:00:00 |
2203.11856 | Analyzing gender is critical to study mental health (MH) support in CVD
(cardiovascular disease). The existing studies on using social media for
extracting MH symptoms consider symptom detection and tend to ignore user
context, disease, or gender. The current study aims to design and evaluate a
system to capture how ... | Usha Lokala, Aseem Srivastava, Triyasha Ghosh Dastidar, Tanmoy
Chakraborty, Md Shad Akthar, Maryam Panahiazar, and Amit Sheth | A Computational Approach to Understand Mental Health from Reddit:
Knowledge-aware Multitask Learning Framework | null | cs.CL cs.AI | 2022-03-23T00:00:00 | 1906.00346 | Medication recommendation is an important healthcare application. It is
commonly formulated as a temporal prediction task. Hence, most existing works
only utilize longitudinal electronic health records (EHRs) from a small number
of patients with multiple visits ignoring a large number of patients with a
single visit ... | Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun | Pre-training of Graph Augmented Transformers for Medication
Recommendation | null | cs.AI cs.CL cs.LG | 2019-11-28T00:00:00 |
1709.01679 | This study addresses the problem of identifying the meaning of unknown words
or entities in a discourse with respect to the word embedding approaches used
in neural language models. We proposed a method for on-the-fly construction and
exploitation of word embeddings in both the input and output layers of a neural
mod... | Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui | A Neural Language Model for Dynamically Representing the Meanings of
Unknown Words and Entities in a Discourse | null | cs.CL | 2017-10-18T00:00:00 | 1611.00196 | In many natural language processing (NLP) tasks, a document is commonly
modeled as a bag of words using the term frequency-inverse document frequency
(TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature
vector is that it ignores word orders that carry syntactic and semantic
relationships among... | Wei Li, Brian Kan Wing Mak | Recurrent Neural Network Language Model Adaptation Derived Document
Vector | null | cs.CL | 2016-12-15T00:00:00 |
1806.05059 | Sequence-to-sequence attention-based models integrate an acoustic,
pronunciation and language model into a single neural network, which make them
very suitable for multilingual automatic speech recognition (ASR). In this
paper, we are concerned with multilingual speech recognition on low-resource
languages by a singl... | Shiyu Zhou, Shuang Xu, Bo Xu | Multilingual End-to-End Speech Recognition with A Single Transformer on
Low-Resource Languages | null | eess.AS cs.CL cs.SD | 2018-06-15T00:00:00 | 1908.10322 | Purely character-based language models (LMs) have been lagging in quality on
large scale datasets, and current state-of-the-art LMs rely on word
tokenization. It has been assumed that injecting the prior knowledge of a
tokenizer into the model is essential to achieving competitive results. In this
paper, we show that... | Dokook Choe, Rami Al-Rfou, Mandy Guo, Heeyoung Lee, Noah Constant | Bridging the Gap for Tokenizer-Free Language Models | null | cs.CL cs.AI cs.IR cs.LG | 2019-08-28T00:00:00 |
2203.00759 | Prompt-Tuning is a new paradigm for finetuning pre-trained language models in
a parameter-efficient way. Here, we explore the use of HyperNetworks to
generate hyper-prompts: we propose HyperPrompt, a novel architecture for
prompt-based task-conditioning of self-attention in Transformers. The
hyper-prompts are end-to-... | Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi
Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed
H. Chi | HyperPrompt: Prompt-based Task-Conditioning of Transformers | null | cs.CL cs.LG | 2022-06-16T00:00:00 | 2410.01487 | Recent work investigates whether LMs learn human-like linguistic
generalizations and representations from developmentally plausible amounts of
data. Yet, the basic linguistic units processed in these LMs are determined by
subword-based tokenization, which limits their validity as models of learning
at and below the w... | Bastian Bunzeck, Daniel Duran, Leonie Schade, Sina Zarrie{\ss} | Small Language Models Also Work With Small Vocabularies: Probing the
Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas | null | cs.CL | 2025-01-07T00:00:00 |
2305.10786 | Prior studies diagnose the anisotropy problem in sentence representations
from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis
reveals that the sentence embeddings from BERT suffer from a bias towards
uninformative words, limiting the performance in semantic textual similarity
(STS) tasks. ... | Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu,
Jiaqing Liu, Yukun Ma, Chong Zhang | Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings | null | cs.CL | 2023-10-24T00:00:00 | 2106.08367 | Transformer-based language models benefit from conditioning on contexts of
hundreds to thousands of previous tokens. What aspects of these contexts
contribute to accurate model prediction? We describe a series of experiments
that measure usable information by selectively ablating lexical and structural
information in... | Joe O'Connor and Jacob Andreas | What Context Features Can Transformer Language Models Use? | null | cs.CL | 2021-06-17T00:00:00 |
2502.11843 | Large Language Models (LLMs) are widely used as conversational agents,
exploiting their capabilities in various sectors such as education, law,
medicine, and more. However, LLMs are often subjected to context-shifting
behaviour, resulting in a lack of consistent and interpretable
personality-aligned interactions. Adh... | Pranav Bhandari and Nicolas Fay and Michael Wise and Amitava Datta and
Stephanie Meek and Usman Naseem and Mehwish Nasim | Can LLM Agents Maintain a Persona in Discourse? | null | cs.CL cs.AI cs.SI | 2025-02-18T00:00:00 | 1907.03064 | We present improvements in automatic speech recognition (ASR) for Somali, a
currently extremely under-resourced language. This forms part of a continuing
United Nations (UN) effort to employ ASR-based keyword spotting systems to
support humanitarian relief programmes in rural Africa. Using just 1.57 hours
of annotate... | Astik Biswas, Raghav Menon, Ewald van der Westhuizen, Thomas Niesler | Improved low-resource Somali speech recognition by semi-supervised
acoustic and language model training | null | cs.CL cs.LG eess.AS | 2019-07-09T00:00:00 |
2202.13047 | Crowdsourced dialogue corpora are usually limited in scale and topic coverage
due to the expensive cost of data curation. This would hinder the
generalization of downstream dialogue models to open-domain topics. In this
work, we leverage large language models for dialogue augmentation in the task
of emotional support... | Chujie Zheng, Sahand Sabour, Jiaxin Wen, Zheng Zhang, Minlie Huang | AugESC: Dialogue Augmentation with Large Language Models for Emotional
Support Conversation | null | cs.CL | 2023-05-19T00:00:00 | 2111.11520 | Open book question answering is a subset of question answering tasks where
the system aims to find answers in a given set of documents (open-book) and
common knowledge about a topic. This article proposes a solution for answering
natural language questions from a corpus of Amazon Web Services (AWS) technical
document... | Sia Gholami and Mehdi Noori | Zero-Shot Open-Book Question Answering | null | cs.CL cs.IR cs.LG | 2021-11-24T00:00:00 |
1601.00248 | Perplexity (per word) is the most widely used metric for evaluating language
models. Despite this, there has been no dearth of criticism for this metric.
Most of these criticisms center around lack of correlation with extrinsic
metrics like word error rate (WER), dependence upon shared vocabulary for model
comparison... | Kushal Arora, Anand Rangarajan | Contrastive Entropy: A new evaluation metric for unnormalized language
models | null | cs.CL | 2016-04-01T00:00:00 | 1804.08881 | Language models have primarily been evaluated with perplexity. While
perplexity quantifies the most comprehensible prediction performance, it does
not provide qualitative information on the success or failure of models.
Another approach for evaluating language models is thus proposed, using the
scaling properties of ... | Shuntaro Takahashi and Kumiko Tanaka-Ishii | Assessing Language Models with Scaling Properties | null | cs.CL | 2018-04-25T00:00:00 |
2203.14465 | Generating step-by-step "chain-of-thought" rationales improves language model
performance on complex reasoning tasks like mathematics or commonsense
question-answering. However, inducing language model rationale generation
currently requires either constructing massive rationale datasets or
sacrificing accuracy by us... | Eric Zelikman, Yuhuai Wu, Jesse Mu, Noah D. Goodman | STaR: Bootstrapping Reasoning With Reasoning | null | cs.LG cs.AI cs.CL | 2022-05-23T00:00:00 | 1711.03953 | We formulate language modeling as a matrix factorization problem, and show
that the expressiveness of Softmax-based models (including the majority of
neural language models) is limited by a Softmax bottleneck. Given that natural
language is highly context-dependent, this further implies that in practice
Softmax with ... | Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen | Breaking the Softmax Bottleneck: A High-Rank RNN Language Model | null | cs.CL cs.LG | 2018-03-06T00:00:00 |
2010.03680 | Sequence labeling is an important technique employed for many Natural
Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot
tagging for dialog systems and semantic parsing. Large-scale pre-trained
language models obtain very good performance on these tasks when fine-tuned on
large amounts of t... | Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu,
Jing Gao, Ahmed Hassan Awadallah | Adaptive Self-training for Few-shot Neural Sequence Labeling | null | cs.CL cs.AI cs.LG | 2020-12-14T00:00:00 | 2305.14793 | Methods to generate text from structured data have advanced significantly in
recent years, primarily due to fine-tuning of pre-trained language models on
large datasets. However, such models can fail to produce output faithful to the
input data, particularly on out-of-domain data. Sufficient annotated data is
often n... | Zhuoer Wang, Marcus Collins, Nikhita Vedula, Simone Filice, Shervin
Malmasi, Oleg Rokhlenko | Faithful Low-Resource Data-to-Text Generation through Cycle Training | null | cs.CL | 2023-07-12T00:00:00 |
2403.10205 | While text summarization is a well-known NLP task, in this paper, we
introduce a novel and useful variant of it called functionality extraction from
Git README files. Though this task is a text2text generation at an abstract
level, it involves its own peculiarities and challenges making existing
text2text generation ... | Prince Kumar, Srikanth Tamilselvam, Dinesh Garg | Read between the lines -- Functionality Extraction From READMEs | null | cs.CL cs.AI | 2024-03-18T00:00:00 | 2206.13749 | On e-commerce platforms, predicting if two products are compatible with each
other is an important functionality to achieve trustworthy product
recommendation and search experience for consumers. However, accurately
predicting product compatibility is difficult due to the heterogeneous product
data and the lack of ma... | Rongzhi Zhang, Rebecca West, Xiquan Cui, Chao Zhang | Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible
Products Prediction | null | cs.LG cs.CL | 2022-06-29T00:00:00 |
1906.01733 | Recent work on Grammatical Error Correction (GEC) has highlighted the
importance of language modeling in that it is certainly possible to achieve
good performance by comparing the probabilities of the proposed edits. At the
same time, advancements in language modeling have managed to generate
linguistic output, which... | Dimitrios Alikaniotis and Vipul Raheja | The Unreasonable Effectiveness of Transformer Language Models in
Grammatical Error Correction | null | cs.CL cs.LG cs.NE | 2019-06-06T00:00:00 | 2310.01041 | Despite the remarkable advances in language modeling, current mainstream
decoding methods still struggle to generate texts that align with human texts
across different aspects. In particular, sampling-based methods produce
less-repetitive texts which are often disjunctive in discourse, while
search-based methods main... | Haozhe Ji, Pei Ke, Hongning Wang, Minlie Huang | Language Model Decoding as Direct Metrics Optimization | The Twelfth International Conference on Learning Representations
(ICLR 2024) | cs.CL | 2024-06-06T00:00:00 |
2106.02902 | Probing complex language models has recently revealed several insights into
linguistic and semantic patterns found in the learned representations. In this
article, we probe BERT specifically to understand and measure the relational
knowledge it captures in its parametric memory. While probing for linguistic
understan... | Jonas Wallat, Jaspreet Singh, Avishek Anand | BERTnesia: Investigating the capture and forgetting of knowledge in BERT | null | cs.CL | 2021-09-09T00:00:00 | 2110.05354 | Text-only adaptation of an end-to-end (E2E) model remains a challenging task
for automatic speech recognition (ASR). Language model (LM) fusion-based
approaches require an additional external LM during inference, significantly
increasing the computation cost. To overcome this, we propose an internal LM
adaptation (IL... | Zhong Meng, Yashesh Gaur, Naoyuki Kanda, Jinyu Li, Xie Chen, Yu Wu,
Yifan Gong | Internal Language Model Adaptation with Text-Only Data for End-to-End
Speech Recognition | Interspeech 2022, Incheon, Korea | cs.CL cs.AI cs.LG cs.SD eess.AS | 2022-11-01T00:00:00 |
1503.05034 | We propose a novel convolutional architecture, named $gen$CNN, for word
sequence prediction. Different from previous work on neural network-based
language modeling and generation (e.g., RNN or LSTM), we choose not to greedily
summarize the history of words as a fixed length vector. Instead, we use a
convolutional neu... | Mingxuan Wang, Zhengdong Lu, Hang Li, Wenbin Jiang, Qun Liu | $gen$CNN: A Convolutional Architecture for Word Sequence Prediction | null | cs.CL | 2015-04-27T00:00:00 | 2103.13610 | Speech-enabled systems typically first convert audio to text through an
automatic speech recognition (ASR) model and then feed the text to downstream
natural language processing (NLP) modules. The errors of the ASR system can
seriously downgrade the performance of the NLP modules. Therefore, it is
essential to make t... | Tong Cui, Jinghui Xiao, Liangyou Li, Xin Jiang, Qun Liu | An Approach to Improve Robustness of NLP Systems against ASR Errors | null | cs.CL | 2021-03-26T00:00:00 |
1302.1123 | The paper revives an older approach to acoustic modeling that borrows from
n-gram language modeling in an attempt to scale up both the amount of training
data and model size (as measured by the number of parameters in the model), to
approximately 100 times larger than current sizes used in automatic speech
recognitio... | Ciprian Chelba, Peng Xu, Fernando Pereira, Thomas Richardson | Large Scale Distributed Acoustic Modeling With Back-off N-grams | null | cs.CL | 2013-02-06T00:00:00 | 2204.10281 | Gender-neutral pronouns have recently been introduced in many languages to a)
include non-binary people and b) as a generic singular. Recent results from
psycholinguistics suggest that gender-neutral pronouns (in Swedish) are not
associated with human processing difficulties. This, we show, is in sharp
contrast with ... | Stephanie Brandl, Ruixiang Cui, Anders S{\o}gaard | How Conservative are Language Models? Adapting to the Introduction of
Gender-Neutral Pronouns | null | cs.CL | 2022-05-04T00:00:00 |
1811.02134 | This work explores better adaptation methods to low-resource languages using
an external language model (LM) under the framework of transfer learning. We
first build a language-independent ASR system in a unified sequence-to-sequence
(S2S) architecture with a shared vocabulary among all languages. During
adaptation, ... | Hirofumi Inaguma, Jaejin Cho, Murali Karthick Baskar, Tatsuya
Kawahara, Shinji Watanabe | Transfer learning of language-independent end-to-end ASR with language
model fusion | null | cs.CL | 2019-05-08T00:00:00 | 1606.03352 | Recently a variety of LSTM-based conditional language models (LM) have been
applied across a range of language generation tasks. In this work we study
various model architectures and different ways to represent and aggregate the
source information in an end-to-end neural dialogue system framework. A method
called sna... | Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Lina M. Rojas-Barahona,
Pei-Hao Su, Stefan Ultes, David Vandyke, Steve Young | Conditional Generation and Snapshot Learning in Neural Dialogue Systems | null | cs.CL cs.NE stat.ML | 2016-06-13T00:00:00 |
1602.06064 | We propose to train bi-directional neural network language model(NNLM) with
noise contrastive estimation(NCE). Experiments are conducted on a rescore task
on the PTB data set. It is shown that NCE-trained bi-directional NNLM
outperformed the one trained by conventional maximum likelihood training. But
still(regretful... | Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu | On Training Bi-directional Neural Network Language Model with Noise
Contrastive Estimation | null | cs.CL | 2016-02-26T00:00:00 | 2409.02060 | We introduce OLMoE, a fully open, state-of-the-art language model leveraging
sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but
uses only 1B per input token. We pretrain it on 5 trillion tokens and further
adapt it to create OLMoE-1B-7B-Instruct. Our models outperform all available
models w... | Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob
Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert,
Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden,
Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah
A. Smith, Pang Wei Koh... | OLMoE: Open Mixture-of-Experts Language Models | null | cs.CL cs.AI cs.LG | 2025-03-04T00:00:00 |
2305.10971 | Africa has over 2000 indigenous languages but they are under-represented in
NLP research due to lack of datasets. In recent years, there have been progress
in developing labeled corpora for African languages. However, they are often
available in a single domain and may not generalize to other domains. In this
paper, ... | Iyanuoluwa Shode, David Ifeoluwa Adelani, Jing Peng, Anna Feldman | NollySenti: Leveraging Transfer Learning and Machine Translation for
Nigerian Movie Sentiment Classification | null | cs.CL | 2023-08-23T00:00:00 | 2002.11268 | This article describes a density ratio approach to integrating external
Language Models (LMs) into end-to-end models for Automatic Speech Recognition
(ASR). Applied to a Recurrent Neural Network Transducer (RNN-T) ASR model
trained on a given domain, a matched in-domain RNN-LM, and a target domain
RNN-LM, the propose... | Erik McDermott, Hasim Sak, Ehsan Variani | A Density Ratio Approach to Language Model Fusion in End-To-End
Automatic Speech Recognition | null | eess.AS cs.CL cs.SD | 2020-03-02T00:00:00 |
2006.12040 | A language model can be used to predict the next word during authoring, to
correct spelling or to accelerate writing (e.g., in sms or emails). Language
models, however, have only been applied in a very small scale to assist
physicians during authoring (e.g., discharge summaries or radiology reports).
But along with t... | John Pavlopoulos and Panagiotis Papapetrou | Clinical Predictive Keyboard using Statistical and Neural Language
Modeling | null | cs.CL | 2020-06-23T00:00:00 | 1908.08529 | Diverse and accurate vision+language modeling is an important goal to retain
creative freedom and maintain user engagement. However, adequately capturing
the intricacies of diversity in language models is challenging. Recent works
commonly resort to latent variable models augmented with more or less
supervision from ... | Jyoti Aneja, Harsh Agrawal, Dhruv Batra, Alexander Schwing | Sequential Latent Spaces for Modeling the Intention During Diverse Image
Captioning | null | cs.CV cs.CL cs.LG stat.ML | 2019-08-23T00:00:00 |
1703.03097 | Extracting useful entities and attribute values from illicit domains such as
human trafficking is a challenging problem with the potential for widespread
social impact. Such domains employ atypical language models, have `long tails'
and suffer from the problem of concept drift. In this paper, we propose a
lightweight... | Mayank Kejriwal, Pedro Szekely | Information Extraction in Illicit Domains | null | cs.CL cs.AI | 2017-03-10T00:00:00 | 2109.12781 | Event extraction in commodity news is a less researched area as compared to
generic event extraction. However, accurate event extraction from commodity
news is useful in abroad range of applications such as under-standing event
chains and learning event-event relations, which can then be used for commodity
price pred... | Meisin Lee, Lay-Ki Soon, Eu-Gene Siew | Effective Use of Graph Convolution Network and Contextual Sub-Tree
forCommodity News Event Extraction | null | cs.CL cs.AI | 2021-09-28T00:00:00 |
cs/0305041 | Factorization of statistical language models is the task that we resolve the
most discriminative model into factored models and determine a new model by
combining them so as to provide better estimate. Most of previous works mainly
focus on factorizing models of sequential events, each of which allows only one
factor... | Wei Wang | Factorization of Language Models through Backing-Off Lattices | null | cs.CL | 2007-05-23T00:00:00 | 1604.01729 | This paper investigates how linguistic knowledge mined from large text
corpora can aid the generation of natural language descriptions of videos.
Specifically, we integrate both a neural language model and distributional
semantics trained on large text corpora into a recent LSTM-based architecture
for video descripti... | Subhashini Venugopalan, Lisa Anne Hendricks, Raymond Mooney, Kate
Saenko | Improving LSTM-based Video Description with Linguistic Knowledge Mined
from Text | Proc.EMNLP (2016) pg.1961-1966 | cs.CL cs.CV | 2016-11-30T00:00:00 |
1602.06064 | We propose to train bi-directional neural network language model(NNLM) with
noise contrastive estimation(NCE). Experiments are conducted on a rescore task
on the PTB data set. It is shown that NCE-trained bi-directional NNLM
outperformed the one trained by conventional maximum likelihood training. But
still(regretful... | Tianxing He, Yu Zhang, Jasha Droppo, Kai Yu | On Training Bi-directional Neural Network Language Model with Noise
Contrastive Estimation | null | cs.CL | 2016-02-26T00:00:00 | cmp-lg/9606002 | In this paper, a hierarchical context definition is added to an existing
clustering algorithm in order to increase its robustness. The resulting
algorithm, which clusters contexts and events separately, is used to experiment
with different ways of defining the context a language model takes into
account. The contexts... | J.P. Ueberla and I.R. Gransden | Clustered Language Models with Context-Equivalent States | null | cmp-lg cs.CL | 2008-02-03T00:00:00 |
2501.06101 | Problem-solving therapy (PST) is a structured psychological approach that
helps individuals manage stress and resolve personal issues by guiding them
through problem identification, solution brainstorming, decision-making, and
outcome evaluation. As mental health care increasingly adopts technologies like
chatbots an... | Elham Aghakhani, Lu Wang, Karla T. Washington, George Demiris, Jina
Huh-Yoo, Rezvaneh Rezapour | From Conversation to Automation: Leveraging LLMs for Problem-Solving
Therapy Analysis | null | cs.CL | 2025-02-20T00:00:00 | 2010.04746 | We solve difficult word-based substitution codes by constructing a decoding
lattice and searching that lattice with a neural language model. We apply our
method to a set of enciphered letters exchanged between US Army General James
Wilkinson and agents of the Spanish Crown in the late 1700s and early 1800s,
obtained ... | Christopher Chu, Raphael Valenti, Kevin Knight | Solving Historical Dictionary Codes with a Neural Language Model | null | cs.CL | 2020-10-13T00:00:00 |
2502.16761 | Large language models (LLMs) present novel opportunities in public opinion
research by predicting survey responses in advance during the early stages of
survey design. Prior methods steer LLMs via descriptions of subpopulations as
LLMs' input prompt, yet such prompt engineering approaches have struggled to
faithfully... | Joseph Suh, Erfan Jahanparast, Suhong Moon, Minwoo Kang, Serina Chang | Language Model Fine-Tuning on Scaled Survey Data for Predicting
Distributions of Public Opinions | null | cs.CL | 2025-02-25T00:00:00 | 1703.02573 | Data noising is an effective technique for regularizing neural network
models. While noising is widely adopted in application domains such as vision
and speech, commonly used noising primitives have not been developed for
discrete sequence-level settings such as language modeling. In this paper, we
derive a connectio... | Ziang Xie, Sida I. Wang, Jiwei Li, Daniel L\'evy, Aiming Nie, Dan
Jurafsky, Andrew Y. Ng | Data Noising as Smoothing in Neural Network Language Models | null | cs.LG cs.CL | 2017-03-09T00:00:00 |
2011.07960 | Syntax is fundamental to our thinking about language. Failing to capture the
structure of input language could lead to generalization problems and
over-parametrization. In the present work, we propose a new syntax-aware
language model: Syntactic Ordered Memory (SOM). The model explicitly models the
structure with an ... | Yikang Shen, Shawn Tan, Alessandro Sordoni, Siva Reddy, Aaron
Courville | Explicitly Modeling Syntax in Language Models with Incremental Parsing
and a Dynamic Oracle | NAACL 2021 | cs.CL cs.LG | 2021-05-12T00:00:00 | 1405.3515 | We provide a method for automatically detecting change in language across
time through a chronologically trained neural language model. We train the
model on the Google Books Ngram corpus to obtain word vector representations
specific to each year, and identify words that have changed significantly from
1900 to 2009.... | Yoon Kim, Yi-I Chiu, Kentaro Hanaki, Darshan Hegde, Slav Petrov | Temporal Analysis of Language through Neural Language Models | Proceedings of the ACL 2014 Workshop on Language Technologies and
Computational Social Science. June, 2014. 61--65 | cs.CL | 2014-08-26T00:00:00 |
2305.18703 | Large language models (LLMs) have significantly advanced the field of natural
language processing (NLP), providing a highly useful, task-agnostic foundation
for a wide range of applications. However, directly applying LLMs to solve
sophisticated problems in specific domains meets many hurdles, caused by the
heterogen... | Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng,
Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao
Zhao, Amit Panalkar, Dhagash Mehta, Stefano Pasquali, Wei Cheng, Haoyu Wang,
Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian
Pei, Carl Yang, and Li... | Domain Specialization as the Key to Make Large Language Models
Disruptive: A Comprehensive Survey | null | cs.CL cs.AI | 2024-04-01T00:00:00 | cs/0006025 | A criterion for pruning parameters from N-gram backoff language models is
developed, based on the relative entropy between the original and the pruned
model. It is shown that the relative entropy resulting from pruning a single
N-gram can be computed exactly and efficiently for backoff models. The relative
entropy me... | A. Stolcke | Entropy-based Pruning of Backoff Language Models | Proceedings DARPA Broadcast News Transcription and Understanding
Workshop, pp. 270-274, Lansdowne, VA, 1998 | cs.CL | 2007-05-23T00:00:00 |
2501.01743 | Legal articles often include vague concepts for adapting to the ever-changing
society. Providing detailed interpretations of these concepts is a critical and
challenging task even for legal practitioners. It requires meticulous and
professional annotations and summarizations by legal experts, which are
admittedly tim... | Kangcheng Luo, Quzhe Huang, Cong Jiang, Yansong Feng | Automating Legal Concept Interpretation with LLMs: Retrieval,
Generation, and Evaluation | null | cs.CL cs.AI | 2025-02-18T00:00:00 | 1711.01048 | In this work, we present a simple and elegant approach to language modeling
for bilingual code-switched text. Since code-switching is a blend of two or
more different languages, a standard bilingual language model can be improved
upon by using structures of the monolingual language models. We propose a novel
techniqu... | Saurabh Garg, Tanmay Parekh, Preethi Jyothi | Dual Language Models for Code Switched Speech Recognition | null | cs.CL | 2018-08-06T00:00:00 |
2205.15172 | Recent work has shown that language models scaled to billions of parameters,
such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In
this work, we experiment with zero-shot models in the legal case entailment
task of the COLIEE 2022 competition. Our experiments show that scaling the
number of p... | Guilherme Moraes Rosa and Luiz Bonifacio and Vitor Jeronymo and Hugo
Abonizio and Roberto Lotufo and Rodrigo Nogueira | Billions of Parameters Are Worth More Than In-domain Training Data: A
case study in the Legal Case Entailment Task | null | cs.CL | 2022-05-31T00:00:00 | 2309.11499 | This paper presents DreamLLM, a learning framework that first achieves
versatile Multimodal Large Language Models (MLLMs) empowered with frequently
overlooked synergy between multimodal comprehension and creation. DreamLLM
operates on two fundamental principles. The first focuses on the generative
modeling of both la... | Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong
Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, Xiangwen Kong,
Xiangyu Zhang, Kaisheng Ma, Li Yi | DreamLLM: Synergistic Multimodal Comprehension and Creation | null | cs.CV cs.CL cs.LG | 2024-03-19T00:00:00 |
2310.02949 | Warning: This paper contains examples of harmful language, and reader
discretion is recommended. The increasing open release of powerful large
language models (LLMs) has facilitated the development of downstream
applications by reducing the essential cost of data annotation and computation.
To ensure AI safety, exten... | Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang,
Xun Zhao, Dahua Lin | Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models | null | cs.CL cs.AI cs.CR cs.LG | 2023-10-05T00:00:00 | 1906.00080 | In this paper, we present Smart Compose, a novel system for generating
interactive, real-time suggestions in Gmail that assists users in writing mails
by reducing repetitive typing. In the design and deployment of such a
large-scale and complicated system, we faced several challenges including model
selection, perfor... | Mia Xu Chen, Benjamin N Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang,
Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy
Sohn, Yonghui Wu | Gmail Smart Compose: Real-Time Assisted Writing | null | cs.CL cs.LG | 2019-06-04T00:00:00 |
1704.08012 | Language models are typically applied at the sentence level, without access
to the broader document context. We present a neural language model that
incorporates document context in the form of a topic model-like architecture,
thus providing a succinct representation of the broader document context
outside of the cur... | Jey Han Lau and Timothy Baldwin and Trevor Cohn | Topically Driven Neural Language Model | In Proceedings of the 55th Annual Meeting of the Association for
Computational Linguistics (ACL 2017), pp. 355--365 | cs.CL | 2017-10-16T00:00:00 | 1505.01809 | Two recent approaches have achieved state-of-the-art results in image
captioning. The first uses a pipelined process where a set of candidate words
is generated by a convolutional neural network (CNN) trained on images, and
then a maximum entropy (ME) language model is used to arrange these words into
a coherent sent... | Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong
He, Geoffrey Zweig, Margaret Mitchell | Language Models for Image Captioning: The Quirks and What Works | null | cs.CL cs.AI cs.CV cs.LG | 2015-10-16T00:00:00 |
2211.07715 | Transformer-based language models have become the standard approach to
solving natural language processing tasks. However, industry adoption usually
requires the maximum throughput to comply with certain latency constraints that
prevents Transformer models from being used in production. To address this gap,
model com... | Haihao Shen, Ofir Zafrir, Bo Dong, Hengyu Meng, Xinyu Ye, Zhe Wang, Yi
Ding, Hanwen Chang, Guy Boudoukh, and Moshe Wasserblat | Fast DistilBERT on CPUs | null | cs.CL cs.AI cs.LG | 2022-12-08T00:00:00 | 2503.08404 | The purpose of this study is to assess how large language models (LLMs) can
be used for fact-checking and contribute to the broader debate on the use of
automated means for veracity identification. To achieve this purpose, we use AI
auditing methodology that systematically evaluates performance of five LLMs
(ChatGPT ... | Elizaveta Kuznetsova, Ilaria Vitulano, Mykola Makhortykh, Martha
Stolze, Tomas Nagy, Victoria Vziatysheva | Fact-checking with Generative AI: A Systematic Cross-Topic Examination
of LLMs Capacity to Detect Veracity of Political Information | null | cs.CL cs.CY | 2025-03-12T00:00:00 |
1805.06087 | Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models,
but when used to generate natural language their output tends to be overly
generic, repetitive, and self-contradictory. We postulate that the objective
function optimized by RNN language models, which amounts to the overall
perplexity of a ... | Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub,
and Yejin Choi | Learning to Write with Cooperative Discriminators | null | cs.CL | 2018-05-17T00:00:00 | 1711.06351 | A hallmark of human intelligence is the ability to ask rich, creative, and
revealing questions. Here we introduce a cognitive model capable of
constructing human-like questions. Our approach treats questions as formal
programs that, when executed on the state of the world, output an answer. The
model specifies a prob... | Anselm Rothe, Brenden M. Lake, Todd M. Gureckis | Question Asking as Program Generation | Rothe, A., Lake, B. M., and Gureckis, T. M. (2017). Question
asking as program generation. Advances in Neural Information Processing
Systems 30 | cs.CL cs.AI cs.LG | 2017-11-20T00:00:00 |
2103.10685 | Large-scale pre-trained language models have demonstrated strong capabilities
of generating realistic text. However, it remains challenging to control the
generation results. Previous approaches such as prompting are far from
sufficient, which limits the usage of language models. To tackle this
challenge, we propose ... | Xu Zou, Da Yin, Qingyang Zhong, Ming Ding, Hongxia Yang, Zhilin Yang,
Jie Tang | Controllable Generation from Pre-trained Language Models via Inverse
Prompting | null | cs.CL cs.AI cs.LG | 2021-11-10T00:00:00 | 1509.08874 | This research explores effects of various training settings between Polish
and English Statistical Machine Translation systems for spoken language.
Various elements of the TED parallel text corpora for the IWSLT 2014 evaluation
campaign were used as the basis for training of language models, and for
development, tuni... | Krzysztof Wo{\l}k, Krzysztof Marasek | Polish - English Speech Statistical Machine Translation Systems for the
IWSLT 2014 | null | cs.CL | 2015-09-30T00:00:00 |
2105.09938 | While programming is one of the most broadly applicable skills in modern
society, modern machine learning models still cannot code solutions to basic
problems. Despite its importance, there has been surprisingly little work on
evaluating code generation, and it can be difficult to accurately assess code
generation pe... | Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika
and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He
and Dawn Song and Jacob Steinhardt | Measuring Coding Challenge Competence With APPS | null | cs.SE cs.CL cs.LG | 2021-11-10T00:00:00 | 2306.11444 | We motivate and formally define a new task for fine-tuning rule-like
generalization in large language models. It is conjectured that the
shortcomings of current LLMs are due to a lack of ability to generalize. It has
been argued that, instead, humans are better at generalization because they
have a tendency at extrac... | Paola Merlo | Blackbird language matrices (BLM), a new task for rule-like
generalization in neural networks: Motivations and Formal Specifications | null | cs.CL | 2023-06-21T00:00:00 |
1707.07413 | In this work, we perform an empirical comparison among the CTC,
RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech
recognition. We show that, without any language model, Seq2Seq and
RNN-Transducer models both outperform the best reported CTC models with a
language model, on the popular Hub5'00 b... | Eric Battenberg, Jitong Chen, Rewon Child, Adam Coates, Yashesh Gaur,
Yi Li, Hairong Liu, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao
Zhu | Exploring Neural Transducers for End-to-End Speech Recognition | null | cs.CL cs.NE | 2017-07-25T00:00:00 | 2104.14690 | Large pre-trained language models (LMs) have demonstrated remarkable ability
as few-shot learners. However, their success hinges largely on scaling model
parameters to a degree that makes it challenging to train and serve. In this
paper, we propose a new approach, named as EFL, that can turn small LMs into
better few... | Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, Hao Ma | Entailment as Few-Shot Learner | null | cs.CL cs.AI | 2021-05-03T00:00:00 |
2410.18798 | Solving complex chart Q&A tasks requires advanced visual reasoning abilities
in multimodal large language models (MLLMs). Recent studies highlight that
these abilities consist of two main parts: recognizing key information from
visual inputs and conducting reasoning over it. Thus, a promising approach to
enhance MLLM... | Wei He, Zhiheng Xi, Wanxu Zhao, Xiaoran Fan, Yiwen Ding, Zifei Shan,
Tao Gui, Qi Zhang, Xuanjing Huang | Distill Visual Chart Reasoning Ability from LLMs to MLLMs | null | cs.CL | 2024-10-25T00:00:00 | 2308.09957 | LLMs like GPT are great at tasks involving English which dominates in their
training data. In this paper, we look at how they cope with tasks involving
languages that are severely under-represented in their training data, in the
context of data-to-text generation for Irish, Maltese, Welsh and Breton. During
the promp... | Michela Lorandi and Anya Belz | Data-to-text Generation for Severely Under-Resourced Languages with
GPT-3.5: A Bit of Help Needed from Google Translate | null | cs.CL cs.AI | 2023-08-22T00:00:00 |
1605.03832 | We introduce polyglot language models, recurrent neural network models
trained to predict symbol sequences in many different languages using shared
representations of symbols and conditioning on typological information about
the language to be predicted. We apply these to the problem of modeling phone
sequences---a d... | Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample,
Patrick Littell, David Mortensen, Alan W Black, Lori Levin and Chris Dyer | Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic
Representation Learning | null | cs.CL | 2016-05-13T00:00:00 | 1705.02669 | Online review communities are dynamic as users join and leave, adopt new
vocabulary, and adapt to evolving trends. Recent work has shown that
recommender systems benefit from explicit consideration of user experience.
However, prior work assumes a fixed number of discrete experience levels,
whereas in reality users g... | Subhabrata Mukherjee, Stephan Guennemann, Gerhard Weikum | Item Recommendation with Continuous Experience Evolution of Users using
Brownian Motion | null | cs.AI cs.CL cs.IR cs.SI stat.ML | 2017-08-10T00:00:00 |
1704.08352 | Words can be represented by composing the representations of subword units
such as word segments, characters, and/or character n-grams. While such
representations are effective and may capture the morphological regularities of
words, they have not been systematically compared, and it is not understood how
they intera... | Clara Vania and Adam Lopez | From Characters to Words to in Between: Do We Capture Morphology? | null | cs.CL | 2017-04-28T00:00:00 | 1803.06456 | We propose two models for a special case of authorship verification problem.
The task is to investigate whether the two documents of a given pair are
written by the same author. We consider the authorship verification problem for
both small and large scale datasets. The underlying small-scale problem has two
main cha... | Marjan Hosseinia and Arjun Mukherjee | Experiments with Neural Networks for Small and Large Scale Authorship
Verification | null | cs.CL | 2018-03-20T00:00:00 |
cmp-lg/9612005 | The Maximum Entropy Modeling Toolkit supports parameter estimation and
prediction for statistical language models in the maximum entropy framework.
The maximum entropy framework provides a constructive method for obtaining the
unique conditional distribution p*(y|x) that satisfies a set of linear
constraints and maxi... | Eric Sven Ristad | Maximum Entropy Modeling Toolkit | null | cmp-lg cs.CL | 2008-02-03T00:00:00 | 2205.15172 | Recent work has shown that language models scaled to billions of parameters,
such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In
this work, we experiment with zero-shot models in the legal case entailment
task of the COLIEE 2022 competition. Our experiments show that scaling the
number of p... | Guilherme Moraes Rosa and Luiz Bonifacio and Vitor Jeronymo and Hugo
Abonizio and Roberto Lotufo and Rodrigo Nogueira | Billions of Parameters Are Worth More Than In-domain Training Data: A
case study in the Legal Case Entailment Task | null | cs.CL | 2022-05-31T00:00:00 |
1511.07788 | Spoken language translation (SLT) is becoming more important in the
increasingly globalized world, both from a social and economic point of view.
It is one of the major challenges for automatic speech recognition (ASR) and
machine translation (MT), driving intense research activities in these areas.
While past resear... | Krzysztof Marasek, {\L}ukasz Brocki, Danijel Korzinek, Krzysztof
Wo{\l}k, Ryszard Gubrynowicz | Spoken Language Translation for Polish | null | cs.CL | 2015-11-25T00:00:00 | 2004.14975 | How does language model pretraining help transfer learning? We consider a
simple ablation technique for determining the impact of each pretrained layer
on transfer task performance. This method, partial reinitialization, involves
replacing different layers of a pretrained model with random weights, then
finetuning th... | Alex Tamkin, Trisha Singh, Davide Giovanardi, Noah Goodman | Investigating Transferability in Pretrained Language Models | null | cs.CL cs.AI cs.LG | 2020-11-11T00:00:00 |
1404.1521 | One of the major research trends currently is the evolution of heterogeneous
parallel computing. GP-GPU computing is being widely used and several
applications have been designed to exploit the massive parallelism that
GP-GPU's have to offer. While GPU's have always been widely used in areas of
computer vision for im... | Vivek Kulkarni, Rami Al-Rfou', Bryan Perozzi, Steven Skiena | Exploring the power of GPU's for training Polyglot language models | null | cs.LG cs.CL | 2014-04-16T00:00:00 | 2307.16230 | Recently, text watermarking algorithms for large language models (LLMs) have
been proposed to mitigate the potential harms of text generated by LLMs,
including fake news and copyright issues. However, current watermark detection
algorithms require the secret key used in the watermark generation process,
making them s... | Aiwei Liu, Leyi Pan, Xuming Hu, Shu'ang Li, Lijie Wen, Irwin King and
Philip S. Yu | An Unforgeable Publicly Verifiable Watermark for Large Language Models | null | cs.CL | 2024-05-28T00:00:00 |
1507.01193 | Recent work on language modelling has shifted focus from count-based models
to neural models. In these works, the words in each sentence are always
considered in a left-to-right order. In this paper we show how we can improve
the performance of the recurrent neural network (RNN) language model by
incorporating the sy... | Piotr Mirowski, Andreas Vlachos | Dependency Recurrent Neural Language Models for Sentence Completion | null | cs.CL cs.AI cs.LG | 2015-07-07T00:00:00 | cmp-lg/9801001 | We describe a simple variant of the interpolated Markov model with
non-emitting state transitions and prove that it is strictly more powerful than
any Markov model. More importantly, the non-emitting model outperforms the
classic interpolated model on the natural language texts under a wide range of
experimental cond... | Eric Sven Ristad and Robert G. Thomas | Hierarchical Non-Emitting Markov Models | null | cmp-lg cs.CL | 2007-05-23T00:00:00 |
cmp-lg/9603002 | Phrase-structure grammars are effective models for important syntactic and
semantic aspects of natural languages, but can be computationally too demanding
for use as language models in real-time speech recognition. Therefore,
finite-state models are used instead, even though they lack expressive power.
To reconcile t... | Fernando C. N. Pereira and Rebecca N. Wright (AT&T Research) | Finite-State Approximation of Phrase-Structure Grammars | null | cmp-lg cs.CL | 2008-02-03T00:00:00 | 2501.12162 | This paper introduces AdaServe, the first LLM serving system to support SLO
customization through fine-grained speculative decoding. AdaServe leverages the
logits of a draft model to predict the speculative accuracy of tokens and
employs a theoretically optimal algorithm to construct token trees for
verification. To ... | Zikun Li, Zhuofu Chen, Remi Delacourt, Gabriele Oliaro, Zeyu Wang,
Qinghan Chen, Shuhuai Lin, April Yang, Zhihao Zhang, Zhuoming Chen, Sean Lai,
Xupeng Miao, Zhihao Jia | AdaServe: SLO-Customized LLM Serving with Fine-Grained Speculative
Decoding | null | cs.CL cs.AI cs.DC cs.LG | 2025-01-22T00:00:00 |
2502.11401 | A new trend uses LLMs as dense text encoders via contrastive learning.
However, since LLM embeddings predict the probability distribution of the next
token, they are inherently generative and distributive, conflicting with
contrastive learning, which requires embeddings to capture full-text semantics
and align via co... | Jingcheng Deng, Zhongtao Jiang, Liang Pang, Liwei Chen, Kun Xu, Zihao
Wei, Huawei Shen, Xueqi Cheng | Following the Autoregressive Nature of LLM Embeddings via Compression
and Alignment | null | cs.CL | 2025-02-28T00:00:00 | 2309.13734 | Stance classification, the task of predicting the viewpoint of an author on a
subject of interest, has long been a focal point of research in domains ranging
from social science to machine learning. Current stance detection methods rely
predominantly on manual annotation of sentences, followed by training a
supervise... | Iain J. Cruickshank and Lynnette Hui Xian Ng | Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance
Classification | null | cs.CL cs.AI | 2024-03-07T00:00:00 |
2503.02911 | Autonomous driving (AD) testing constitutes a critical methodology for
assessing performance benchmarks prior to product deployment. The creation of
segmented scenarios within a simulated environment is acknowledged as a robust
and effective strategy; however, the process of tailoring these scenarios often
necessitat... | Xuan Cai, Xuesong Bai, Zhiyong Cui, Danmu Xie, Daocheng Fu, Haiyang
Yu, Yilong Ren | Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving
Test | null | cs.SE cs.AI cs.CL | 2025-03-06T00:00:00 | 2203.00759 | Prompt-Tuning is a new paradigm for finetuning pre-trained language models in
a parameter-efficient way. Here, we explore the use of HyperNetworks to
generate hyper-prompts: we propose HyperPrompt, a novel architecture for
prompt-based task-conditioning of self-attention in Transformers. The
hyper-prompts are end-to-... | Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi
Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed
H. Chi | HyperPrompt: Prompt-based Task-Conditioning of Transformers | null | cs.CL cs.LG | 2022-06-16T00:00:00 |
2412.18139 | The in-image machine translation task involves translating text embedded
within images, with the translated results presented in image format. While
this task has numerous applications in various scenarios such as film poster
translation and everyday scene image translation, existing methods frequently
neglect the as... | Chengpeng Fu, Xiaocheng Feng, Yichong Huang, Wenshuai Huo, Baohang Li,
Zhirui Zhang, Yunfei Lu, Dandan Tu, Duyu Tang, Hui Wang, Bing Qin, Ting Liu | Ensuring Consistency for In-Image Translation | null | cs.CL | 2024-12-25T00:00:00 | 2105.09938 | While programming is one of the most broadly applicable skills in modern
society, modern machine learning models still cannot code solutions to basic
problems. Despite its importance, there has been surprisingly little work on
evaluating code generation, and it can be difficult to accurately assess code
generation pe... | Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika
and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He
and Dawn Song and Jacob Steinhardt | Measuring Coding Challenge Competence With APPS | null | cs.SE cs.CL cs.LG | 2021-11-10T00:00:00 |
2112.06377 | Fast-developing fields such as Artificial Intelligence (AI) often outpace the
efforts of encyclopedic sources such as Wikipedia, which either do not
completely cover recently-introduced topics or lack such content entirely. As a
result, methods for automatically producing content are valuable tools to
address this in... | Irene Li, Alexander Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang,
Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, Dragomir Radev | Surfer100: Generating Surveys From Web Resources, Wikipedia-style | null | cs.CL cs.LG | 2022-06-23T00:00:00 | cmp-lg/9706007 | We consider the use of language models whose size and accuracy are
intermediate between different order n-gram models. Two types of models are
studied in particular. Aggregate Markov models are class-based bigram models in
which the mapping from words to classes is probabilistic. Mixed-order Markov
models combine big... | Lawrence Saul and Fernando Pereira (AT&T Labs -- Research) | Aggregate and mixed-order Markov models for statistical language
processing | null | cmp-lg cs.CL | 2008-02-03T00:00:00 |
1911.03090 | Pretrained transformer-based language models have achieved state of the art
across countless tasks in natural language processing. These models are highly
expressive, comprising at least a hundred million parameters and a dozen
layers. Recent evidence suggests that only a few of the final layers need to be
fine-tuned... | Jaejun Lee, Raphael Tang, Jimmy Lin | What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning | null | cs.CL | 2019-11-11T00:00:00 | 1905.05583 | Language model pre-training has proven to be useful in learning universal
language representations. As a state-of-the-art language model pre-training
model, BERT (Bidirectional Encoder Representations from Transformers) has
achieved amazing results in many language understanding tasks. In this paper,
we conduct exhau... | Chi Sun, Xipeng Qiu, Yige Xu, Xuanjing Huang | How to Fine-Tune BERT for Text Classification? | null | cs.CL | 2020-02-06T00:00:00 |
1908.09203 | Large language models have a range of beneficial uses: they can assist in
prose, poetry, and programming; analyze dataset biases; and more. However,
their flexibility and generative capabilities also raise misuse concerns. This
report discusses OpenAI's work related to the release of its GPT-2 language
model. It disc... | Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel
Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah
Kreps, Miles McCain, Alex Newhouse, Jason Blazakis, Kris McGuffie, Jasmine
Wang | Release Strategies and the Social Impacts of Language Models | null | cs.CL cs.AI cs.CY | 2019-11-14T00:00:00 | 2403.12285 | There are multiple sources of financial news online which influence market
movements and trader's decisions. This highlights the need for accurate
sentiment analysis, in addition to having appropriate algorithmic trading
techniques, to arrive at better informed trading decisions. Standard lexicon
based sentiment appr... | Thanos Konstantinidis, Giorgos Iacovides, Mingxue Xu, Tony G.
Constantinides, Danilo Mandic | FinLlama: Financial Sentiment Classification for Algorithmic Trading
Applications | null | cs.CL cs.LG q-fin.ST q-fin.TR | 2024-03-20T00:00:00 |
1907.05774 | Data scarcity is a long-standing and crucial challenge that hinders quick
development of task-oriented dialogue systems across multiple domains:
task-oriented dialogue models are expected to learn grammar, syntax, dialogue
reasoning, decision making, and language generation from absurdly small amounts
of task-specifi... | Pawe{\l} Budzianowski and Ivan Vuli\'c | Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained
Language Models for Task-Oriented Dialogue Systems | null | cs.CL | 2019-08-06T00:00:00 | 2201.11473 | Reasoning over natural language is a long-standing goal for the research
community. However, studies have shown that existing language models are
inadequate in reasoning. To address the issue, we present POET, a novel
reasoning pre-training paradigm. Through pre-training language models with
programs and their execut... | Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan
Gao, Jian-Guang Lou, Weizhu Chen | Reasoning Like Program Executors | null | cs.CL cs.AI cs.SC | 2022-10-25T00:00:00 |
2310.17407 | Can a machine understand the meanings of natural language? Recent
developments in the generative large language models (LLMs) of artificial
intelligence have led to the belief that traditional philosophical assumptions
about machine understanding of language need to be revised. This article
critically evaluates the p... | Vladim\'ir Havl\'ik | Meaning and understanding in large language models | null | cs.CL | 2023-10-27T00:00:00 | 1601.00248 | Perplexity (per word) is the most widely used metric for evaluating language
models. Despite this, there has been no dearth of criticism for this metric.
Most of these criticisms center around lack of correlation with extrinsic
metrics like word error rate (WER), dependence upon shared vocabulary for model
comparison... | Kushal Arora, Anand Rangarajan | Contrastive Entropy: A new evaluation metric for unnormalized language
models | null | cs.CL | 2016-04-01T00:00:00 |
2502.06207 | Large Language Models (LLMs) have become essential for offensive language
detection, yet their ability to handle annotation disagreement remains
underexplored. Disagreement samples, which arise from subjective
interpretations, pose a unique challenge due to their ambiguous nature.
Understanding how LLMs process these... | Junyu Lu, Kai Ma, Kaichun Wang, Kelaiti Xiao, Roy Ka-Wei Lee, Bo Xu,
Liang Yang, Hongfei Lin | Unveiling the Capabilities of Large Language Models in Detecting
Offensive Language with Annotation Disagreement | null | cs.CL cs.AI | 2025-02-18T00:00:00 | 0907.1814 | We present BayeSum (for ``Bayesian summarization''), a model for sentence
extraction in query-focused summarization. BayeSum leverages the common case in
which multiple documents are relevant to a single query. Using these documents
as reinforcement for query terms, BayeSum is not afflicted by the paucity of
informat... | Hal Daum\'e III | Bayesian Query-Focused Summarization | ACL 2006 | cs.CL cs.IR cs.LG | 2009-07-13T00:00:00 |
1501.05203 | Reordering is a challenge to machine translation (MT) systems. In MT, the
widely used approach is to apply word based language model (LM) which considers
the constituent units of a sentence as words. In speech recognition (SR), some
phrase based LM have been proposed. However, those LMs are not necessarily
suitable o... | Geliang Chen | Phrase Based Language Model for Statistical Machine Translation:
Empirical Study | null | cs.CL | 2015-02-19T00:00:00 | 1809.00042 | RNN language models have achieved state-of-the-art perplexity results and
have proven useful in a suite of NLP tasks, but it is as yet unclear what
syntactic generalizations they learn. Here we investigate whether
state-of-the-art RNN language models represent long-distance filler-gap
dependencies and constraints on ... | Ethan Wilcox, Roger Levy, Takashi Morita and Richard Futrell | What do RNN Language Models Learn about Filler-Gap Dependencies? | null | cs.CL | 2018-09-05T00:00:00 |
2106.02834 | Pre-trained multilingual language models (LMs) have achieved state-of-the-art
results in cross-lingual transfer, but they often lead to an inequitable
representation of languages due to limited capacity, skewed pre-training data,
and sub-optimal vocabularies. This has prompted the creation of an ever-growing
pre-trai... | Simran Khanuja, Melvin Johnson, Partha Talukdar | MergeDistill: Merging Pre-trained Language Models using Distillation | null | cs.CL | 2021-06-08T00:00:00 | 2502.11843 | Large Language Models (LLMs) are widely used as conversational agents,
exploiting their capabilities in various sectors such as education, law,
medicine, and more. However, LLMs are often subjected to context-shifting
behaviour, resulting in a lack of consistent and interpretable
personality-aligned interactions. Adh... | Pranav Bhandari and Nicolas Fay and Michael Wise and Amitava Datta and
Stephanie Meek and Usman Naseem and Mehwish Nasim | Can LLM Agents Maintain a Persona in Discourse? | null | cs.CL cs.AI cs.SI | 2025-02-18T00:00:00 |
cmp-lg/9606021 | We present an iterative procedure to build a Chinese language model (LM). We
segment Chinese text into words based on a word-based Chinese language model.
However, the construction of a Chinese LM itself requires word boundaries. To
get out of the chicken-and-egg problem, we propose an iterative procedure that
altern... | Xiaoqiang Luo (Center for Language and Speech Processing, The Johns
Hopkins University) and Salim Roukos (IBM T. J. Watson Research Center) | An Iterative Algorithm to Build Chinese Language Models | null | cmp-lg cs.CL | 2008-02-03T00:00:00 | 2306.01506 | Self-supervised techniques for learning speech representations have been
shown to develop linguistic competence from exposure to speech without the need
for human labels. In order to fully realize the potential of these approaches
and further our understanding of how infants learn language, simulations must
closely e... | Marvin Lavechin and Yaya Sy and Hadrien Titeux and Mar\'ia Andrea Cruz
Bland\'on and Okko R\"as\"anen and Herv\'e Bredin and Emmanuel Dupoux and
Alejandrina Cristia | BabySLM: language-acquisition-friendly benchmark of self-supervised
spoken language models | null | cs.CL eess.AS stat.ML | 2025-03-12T00:00:00 |
2404.17283 | Retrieval-augmented language models have exhibited promising performance
across various areas of natural language processing (NLP), including
fact-critical tasks. However, due to the black-box nature of advanced large
language models (LLMs) and the non-retrieval-oriented supervision signal of
specific tasks, the trai... | Xuan Zhang and Wei Gao | Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact
Checking News Claims with Black-Box LLM | null | cs.CL | 2024-04-29T00:00:00 | 1910.14549 | Semantic parsing is the task of transforming sentences from natural language
into formal representations of predicate-argument structures. Under this
research area, frame-semantic parsing has attracted much interest. This parsing
approach leverages the lexical information defined in FrameNet to associate
marked predi... | Sang-Sang Tan (1), Jin-Cheon Na (1) ((1) Nanyang Technological
University, Singapore) | Positional Attention-based Frame Identification with BERT: A Deep
Learning Approach to Target Disambiguation and Semantic Frame Selection | null | cs.CL cs.LG | 2019-11-01T00:00:00 |
0907.1814 | We present BayeSum (for ``Bayesian summarization''), a model for sentence
extraction in query-focused summarization. BayeSum leverages the common case in
which multiple documents are relevant to a single query. Using these documents
as reinforcement for query terms, BayeSum is not afflicted by the paucity of
informat... | Hal Daum\'e III | Bayesian Query-Focused Summarization | ACL 2006 | cs.CL cs.IR cs.LG | 2009-07-13T00:00:00 | 2305.10786 | Prior studies diagnose the anisotropy problem in sentence representations
from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis
reveals that the sentence embeddings from BERT suffer from a bias towards
uninformative words, limiting the performance in semantic textual similarity
(STS) tasks. ... | Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu,
Jiaqing Liu, Yukun Ma, Chong Zhang | Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings | null | cs.CL | 2023-10-24T00:00:00 |
2310.01041 | Despite the remarkable advances in language modeling, current mainstream
decoding methods still struggle to generate texts that align with human texts
across different aspects. In particular, sampling-based methods produce
less-repetitive texts which are often disjunctive in discourse, while
search-based methods main... | Haozhe Ji, Pei Ke, Hongning Wang, Minlie Huang | Language Model Decoding as Direct Metrics Optimization | The Twelfth International Conference on Learning Representations
(ICLR 2024) | cs.CL | 2024-06-06T00:00:00 | 2302.05900 | Text generation from Abstract Meaning Representation (AMR) has substantially
benefited from the popularized Pretrained Language Models (PLMs). Myriad
approaches have linearized the input graph as a sequence of tokens to fit the
PLM tokenization requirements. Nevertheless, this transformation jeopardizes
the structura... | Sebastien Montella, Alexis Nasr, Johannes Heinecke, Frederic Bechet,
Lina M. Rojas-Barahona | Investigating the Effect of Relative Positional Embeddings on
AMR-to-Text Generation with Structural Adapters | null | cs.CL | 2023-02-14T00:00:00 |
1807.06441 | Recently, recurrent neural networks have become state-of-the-art in acoustic
modeling for automatic speech recognition. The long short-term memory (LSTM)
units are the most popular ones. However, alternative units like gated
recurrent unit (GRU) and its modifications outperformed LSTM in some
publications. In this pa... | Jan Vanek, Josef Michalek, Jan Zelinka, Josef Psutka | A Comparison of Adaptation Techniques and Recurrent Neural Network
Architectures | null | eess.AS cs.CL cs.SD | 2018-07-18T00:00:00 | 1911.03937 | Unsupervised neural machine translation(NMT) is associated with noise and
errors in synthetic data when executing vanilla back-translations. Here, we
explicitly exploits language model(LM) to drive construction of an unsupervised
NMT system. This features two steps. First, we initialize NMT models using
synthetic dat... | Wei Zhang, Youyuan Lin, Ruoran Ren, Xiaodong Wang, Zhenshuang Liang,
Zhen Huang | Language Model-Driven Unsupervised Neural Machine Translation | null | cs.CL | 2019-11-12T00:00:00 |
2109.03570 | This work presents biomedical and clinical language models for Spanish by
experimenting with different pretraining choices, such as masking at word and
subword level, varying the vocabulary size and testing with domain data,
looking for better language representations. Interestingly, in the absence of
enough clinical... | Casimiro Pio Carrino, Jordi Armengol-Estap\'e, Asier
Guti\'errez-Fandi\~no, Joan Llop-Palao, Marc P\`amies, Aitor Gonzalez-Agirre,
Marta Villegas | Biomedical and Clinical Language Models for Spanish: On the Benefits of
Domain-Specific Pretraining in a Mid-Resource Scenario | null | cs.CL | 2021-09-20T00:00:00 | 1603.03185 | We describe a large vocabulary speech recognition system that is accurate,
has low latency, and yet has a small enough memory and computational footprint
to run faster than real-time on a Nexus 5 Android smartphone. We employ a
quantized Long Short-Term Memory (LSTM) acoustic model trained with
connectionist temporal... | Ian McGraw, Rohit Prabhavalkar, Raziel Alvarez, Montse Gonzalez
Arenas, Kanishka Rao, David Rybach, Ouais Alsharif, Hasim Sak, Alexander
Gruenstein, Francoise Beaufays, Carolina Parada | Personalized Speech recognition on mobile devices | null | cs.CL cs.LG cs.SD | 2016-03-15T00:00:00 |
1707.00117 | This paper presents a Semantic Attribute Modulation (SAM) for language
modeling and style variation. The semantic attribute modulation includes
various document attributes, such as titles, authors, and document categories.
We consider two types of attributes, (title attributes and category
attributes), and a flexible... | Wenbo Hu, Lifeng Hua, Lei Li, Hang Su, Tian Wang, Ning Chen, Bo Zhang | SAM: Semantic Attribute Modulation for Language Modeling and Style
Variation | null | cs.CL cs.LG stat.ML | 2017-09-15T00:00:00 | 2004.03090 | Existing conversational datasets consist either of written proxies for dialog
or small-scale transcriptions of natural speech. We introduce 'Interview': a
large-scale (105K conversations) media dialog dataset collected from news
interview transcripts. Compared to existing large-scale proxies for
conversational data, ... | Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley | Interview: A Large-Scale Open-Source Corpus of Media Dialog | null | cs.CL | 2020-04-08T00:00:00 |
1806.00913 | Self-normalizing discriminative models approximate the normalized probability
of a class without having to compute the partition function. In the context of
language modeling, this property is particularly appealing as it may
significantly reduce run-times due to large word vocabularies. In this study,
we provide a c... | Jacob Goldberger and Oren Melamud | Self-Normalization Properties of Language Modeling | null | cs.CL | 2018-06-05T00:00:00 | 1606.06031 | We introduce LAMBADA, a dataset to evaluate the capabilities of computational
models for text understanding by means of a word prediction task. LAMBADA is a
collection of narrative passages sharing the characteristic that human subjects
are able to guess their last word if they are exposed to the whole passage, but
n... | Denis Paperno (1), Germ\'an Kruszewski (1), Angeliki Lazaridou (1),
Quan Ngoc Pham (1), Raffaella Bernardi (1), Sandro Pezzelle (1), Marco Baroni
(1), Gemma Boleda (1), Raquel Fern\'andez (2) ((1) CIMeC - Center for
Mind/Brain Sciences, University of Trento, (2) Institute for Logic, Language
& Computation, Univ... | The LAMBADA dataset: Word prediction requiring a broad discourse context | null | cs.CL cs.AI cs.LG | 2016-06-21T00:00:00 |
2109.00025 | Sense representations have gone beyond word representations like Word2Vec,
GloVe and FastText and achieved innovative performance on a wide range of
natural language processing tasks. Although very useful in many applications,
the traditional approaches for generating word embeddings have a strict
drawback: they prod... | Jessica Rodrigues da Silva, Helena de Medeiros Caseli | Sense representations for Portuguese: experiments with sense embeddings
and deep neural language models | Language Resources and Evaluation (2021) | cs.CL cs.LG | 2021-09-02T00:00:00 | 2205.12506 | Large language models are shown to present privacy risks through memorization
of training data, and several recent works have studied such risks for the
pre-training phase. Little attention, however, has been given to the
fine-tuning phase and it is not well understood how different fine-tuning
methods (such as fine-... | Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans,
Taylor Berg-Kirkpatrick | Memorization in NLP Fine-tuning Methods | null | cs.CL cs.LG | 2022-11-07T00:00:00 |
1302.2569 | We propose a new statistical model for computational linguistics. Rather than
trying to estimate directly the probability distribution of a random sentence
of the language, we define a Markov chain on finite sets of sentences with many
finite recurrent communicating classes and define our language model as the
invari... | Olivier Catoni and Thomas Mainguy | Toric grammars: a new statistical approach to natural language modeling | null | stat.ML cs.CL math.PR | 2013-02-12T00:00:00 | 2308.06039 | In learning to defer, a predictor identifies risky decisions and defers them
to a human expert. One key issue with this setup is that the expert may end up
over-relying on the machine's decisions, due to anchoring bias. At the same
time, whenever the machine chooses the deferral option the expert has to take
decision... | Debodeep Banerjee, Stefano Teso, Andrea Passerini | Learning to Guide Human Experts via Personalized Large Language Models | null | cs.AI cs.CL | 2023-08-14T00:00:00 |
2004.01881 | In this paper, we formulate a more realistic and difficult problem setup for
the intent detection task in natural language understanding, namely Generalized
Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a joint label
space consisting of both existing intents which have enough labeled data and
novel in... | Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, Philip Yu | CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot
Intent Detection | null | cs.CL cs.LG | 2020-04-07T00:00:00 | 1511.06391 | Sequences have become first class citizens in supervised learning thanks to
the resurgence of recurrent neural networks. Many complex tasks that require
mapping from or to a sequence of observations can now be formulated with the
sequence-to-sequence (seq2seq) framework which employs the chain rule to
efficiently rep... | Oriol Vinyals, Samy Bengio, Manjunath Kudlur | Order Matters: Sequence to sequence for sets | null | stat.ML cs.CL cs.LG | 2016-02-25T00:00:00 |
1707.05266 | In this study, we introduce a new approach for learning language models by
training them to estimate word-context pointwise mutual information (PMI), and
then deriving the desired conditional probabilities from PMI at test time.
Specifically, we show that with minor modifications to word2vec's algorithm, we
get princ... | Oren Melamud, Ido Dagan, Jacob Goldberger | A Simple Language Model based on PMI Matrix Approximations | null | cs.CL | 2017-07-18T00:00:00 | 1809.08731 | Motivated by recent findings on the probabilistic modeling of acceptability
judgments, we propose syntactic log-odds ratio (SLOR), a normalized language
model score, as a metric for referenceless fluency evaluation of natural
language generation output at the sentence level. We further introduce WPSLOR,
a novel WordP... | Katharina Kann, Sascha Rothe and Katja Filippova | Sentence-Level Fluency Evaluation: References Help, But Can Be Spared! | null | cs.CL | 2018-09-25T00:00:00 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.