context stringlengths 3.85k 99.8k | questions listlengths 1 12 | answers listlengths 1 12 |
|---|---|---|
Although Neural Machine Translation (NMT) has dominated recent research on translation tasks BIBREF0, BIBREF1, BIBREF2, NMT heavily relies on large-scale parallel data, resulting in poor performance on low-resource or zero-resource language pairs BIBREF3. Translation between these low-resource languages (e.g., Arabic$\... | [
"which multilingual approaches do they compare with?",
"what are the pivot-based baselines?",
"which datasets did they experiment with?",
"what language pairs are explored?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"De-En, En-Fr, Fr-En, En-Es, Ro-En, En-De, Ar-En, En-Ru",
""
]
] |
Named entity recognition is an important task of natural language processing, featuring in many popular text processing toolkits. This area of natural language processing has been actively studied in the latest decades and the advent of deep learning reinvigorated the research on more effective and accurate models. How... | [
"what ner models were evaluated?",
"what is the source of the news sentences?",
"did they use a crowdsourcing platform for manual annotations?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
“I'm supposed to trust the opinion of a MS minion? The people that produced Windows ME, Vista and 8? They don't even understand people, yet they think they can predict the behavior of new, self-guiding AI?” –anonymous“I think an AI would make it easier for Patients to confide their information because by nature, a robo... | [
"what are the topics pulled from Reddit?",
"What predictive model do they build?"
] | [
[
"",
"training data has posts from politics, business, science and other popular topics; the trained model is applied to millions of unannotated posts on all of Reddit"
],
[
"",
""
]
] |
There has been significant progress on Named Entity Recognition (NER) in recent years using models based on machine learning algorithms BIBREF0 , BIBREF1 , BIBREF2 . As with other Natural Language Processing (NLP) tasks, building NER systems typically requires a massive amount of labeled training data which are annotat... | [
"What accuracy does the proposed system achieve?",
"What crowdsourcing platform is used?"
] | [
[
"F1 scores of 85.99 on the DL-PS data, 75.15 on the EC-MT data and 71.53 on the EC-UQ data ",
"F1 of 85.99 on the DL-PS dataset (dialog domain); 75.15 on EC-MT and 71.53 on EC-UQ (e-commerce domain)"
],
[
"",
"They did not use any platform, instead they hired undergraduate students to do the ... |
Deep Learning approaches have achieved impressive results on various NLP tasks BIBREF0 , BIBREF1 , BIBREF2 and have become the de facto approach for any NLP task. However, these deep learning techniques have found to be less effective for low-resource languages when the available training data is very less BIBREF3 . Re... | [
"How do they match words before reordering them?",
"On how many language pairs do they show that preordering assisting language sentences helps translation quality?",
"Which dataset(s) do they experiment with?"
] | [
[
"",
""
],
[
"5",
""
],
[
"",
""
]
] |
Simplified language is a variety of standard language characterized by reduced lexical and syntactic complexity, the addition of explanations for difficult concepts, and clearly structured layout. Among the target groups of simplified language commonly mentioned are persons with cognitive impairment or learning disabil... | [
"Which information about text structure is included in the corpus?",
"Which information about typography is included in the corpus?"
] | [
[
"",
"paragraph, lines, textspan element (paragraph segmentation, line segmentation, Information on physical page segmentation(for PDF only))"
],
[
"",
""
]
] |
Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB. Figure 1 illustrates the proc... | [
"On which benchmarks they achieve the state of the art?",
"What they use in their propsoed framework?",
"What does KBQA abbreviate for",
"What is te core component for KBQA?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
The application of deep learning methods to NLP is made possible by representing words as vectors in a low-dimensional continuous space. Traditionally, these word embeddings were static: each word had a single vector, regardless of context BIBREF0, BIBREF1. This posed several problems, most notably that all senses of a... | [
"What experiments are proposed to test that upper layers produce context-specific embeddings?",
"How do they calculate a static embedding for each word?"
] | [
[
"They measure self-similarity, intra-sentence similarity and maximum explainable variance of the embeddings in the upper layers.",
"They plot the average cosine similarity between uniformly random words increases exponentially from layers 8 through 12. \nThey plot the average self-similarity of uniformly... |
During the first two decades of the 21st century, the sharing and processing of vast amounts of data has become pervasive. This expansion of data sharing and processing capabilities is both a blessing and a curse. Data helps build better information systems for the digital era and enables further research for advanced ... | [
"What is the performance of BERT on the task?",
"What are the other algorithms tested?",
"Does BERT reach the best performance among all the algorithms compared?",
"What are the clinical datasets used in the paper?"
] | [
[
"F1 scores are:\nHUBES-PHI: Detection(0.965), Classification relaxed (0.95), Classification strict (0.937)\nMedoccan: Detection(0.972), Classification (0.967)",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Accurate grapheme-to-phoneme conversion (g2p) is important for any application that depends on the sometimes inconsistent relationship between spoken and written language. Most prominently, this includes text-to-speech and automatic speech recognition. Most work on g2p has focused on a few languages for which extensive... | [
"how is model compactness measured?",
"what was the baseline?",
"what evaluation metrics were used?",
"what datasets did they use?"
] | [
[
"Using file size on disk",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Recently, with the emergence of neural seq2seq models, abstractive summarization methods have seen great performance strides BIBREF0, BIBREF1, BIBREF2. However, complex neural summarization models with thousands of parameters usually require a large amount of training data. In fact, much of the neural summarization wor... | [
"What is the interannotator agreement for the human evaluation?",
"Who were the human evaluators used?",
"Is the template-based model realistic? ",
"Is the student reflection data very different from the newspaper data? ",
"What is the recent abstractive summarization method in this paper?"
] | [
[
"",
""
],
[
"",
"20 annotatos from author's institution"
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Recently, contextual-aware language models such as ELMo BIBREF0, GPT BIBREF1, BERT BIBREF2 and XLNet BIBREF3 have shown to greatly outperform traditional word embedding models including Word2Vec BIBREF4 and GloVe BIBREF5 in a variety of NLP tasks. These pre-trained language models, when fine-tuned on downstream languag... | [
"Why are prior knowledge distillation techniques models are ineffective in producing student models with vocabularies different from the original teacher models? ",
"What state-of-the-art compression techniques were used in the comparison?"
] | [
[
"",
""
],
[
"",
""
]
] |
The Flickr30K dataset BIBREF0 is a collection of over 30,000 images with 5 crowdsourced descriptions each. It is commonly used to train and evaluate neural network models that generate image descriptions (e.g. BIBREF2 ). An untested assumption behind the dataset is that the descriptions are based on the images, and not... | [
"What evaluations methods do they take?",
"What is the size of the dataset?",
"Which methods are considered to find examples of biases and unwarranted inferences??",
"What biases are found in the dataset?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
"Looking for adjectives marking the noun \"baby\" and also looking for most-common adjectives related to certain nouns using POS-tagging"
],
[
"Ethnic bias",
""
]
] |
PDTB-style discourse relations, mostly defined between two adjacent text spans (i.e., discourse units, either clauses or sentences), specify how two discourse units are logically connected (e.g., causal, contrast). Recognizing discourse relations is one crucial step in discourse analysis and can be beneficial for many ... | [
"What discourse relations does it work best/worst for?",
"How much does this model improve state-of-the-art?"
] | [
[
"",
"Best: Expansion (Exp). Worst: Comparison (Comp)."
],
[
"",
""
]
] |
In recent years, there has been an increasing interest in Machine reading comprehension (MRC), which plays a vital role in the assessment of how well a machine could understand natural language. Several datasets BIBREF0 , BIBREF1 , BIBREF2 for machine reading comprehension have been released in recent years and have dr... | [
"Where is a question generation model used?"
] | [
[
"",
""
]
] |
The recently introduced BERT model BIBREF0 exhibits strong performance on several language understanding benchmarks. To what extent does it capture syntax-sensitive structures?Recent work examines the extent to which RNN-based models capture syntax-sensitive phenomena that are traditionally taken as evidence for the ex... | [
"Were any of these tasks evaluated in any previous work?"
] | [
[
"",
""
]
] |
Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is be... | [
"Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people?",
"Which demographic dimensions of people do they obtain?",
"How do they obtain psychological dimensions of people?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
To appear in Proceedings of International Workshop on Health Intelligence (W3PHIAI) of the 34th AAAI Conference on Artificial Intelligence, 2020.Physician burnout is a growing concern, estimated to be experienced by at least 35% of physicians in the developing world and 50% in the United States BIBREF0. BIBREF1 found t... | [
"What is the baseline?",
"Is the data de-identified?",
"What embeddings are used?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Bidirectional Encoder Representations from Transformers (BERT) is a novel Transformer BIBREF0 model, which recently achieved state-of-the-art performance in several language understanding tasks, such as question answering, natural language inference, semantic similarity, sentiment analysis, and others BIBREF1. While we... | [
"What datasets did they use for evaluation?",
"On top of BERT does the RNN layer work better or the transformer layer?"
] | [
[
"",
""
],
[
"",
"The transformer layer"
]
] |
Nowadays, dialog systems are usually designed for a single domain BIBREF0 . They store data in a well-defined format with a fixed number of attributes for entities that the system can provide. Because data in this format can be stored as a two-dimensional table within a relational database, we call the data flat. This ... | [
"How was this data collected?",
"What is the average length of dialog?"
] | [
[
"",
"The crowdsourcing platform CrowdFlower was used to obtain natural dialog data that prompted the user to paraphrase, explain, and/or answer a question from a Simple questions BIBREF7 dataset. The CrowdFlower users were restricted to English-speaking countries to avoid dialogs with poor English."
],... |
Suppose a user wants to write a sentence “I will be 10 minutes late.” Ideally, she would type just a few keywords such as “10 minutes late” and an autocomplete system would be able to infer the intended sentence (Figure FIGREF1). Existing left-to-right autocomplete systems BIBREF0, BIBREF1 can often be inefficient, as ... | [
"How are models evaluated in this human-machine communication game?",
"How many participants were trying this communication game?",
"What user variations have been tested?",
"What are the baselines used?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Robotic Process Automation (RPA) is a type of software bots that simulates hand-operated human activities like entering data into a system, registering into accounts, and accomplishing straightforward but repetitive workflows BIBREF0. However, one of the drawbacks of RPA-bots is their susceptibility to changes in defin... | [
"Do they use off-the-shelf NLP systems to build their assitant?",
"How does the IPA label data after interacting with users?",
"What kind of repetitive and time-consuming activities does their assistant handle?"
] | [
[
"",
""
],
[
"It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13 custom actions defined).",
""
],
[
"",
""
]
] |
The idea of language identification is to classify a given audio signal into a particular class using a classification algorithm. Commonly language identification task was done using i-vector systems [1]. A very well known approach for language identification proposed by N. Dahek et al. [1] uses the GMM-UBM model to ob... | [
"How was the audio data gathered?",
"What is the GhostVLAD approach?",
"Which 7 Indian languages do they experiment with?"
] | [
[
"Through the All India Radio new channel where actors read news.",
""
],
[
"",
"An extension of NetVLAD which replaces hard assignment-based clustering with soft assignment-based clustering with the additon o fusing Ghost clusters to deal with noisy content."
],
[
"Hindi, English, Kann... |
Data annotation is a major bottleneck for the application of supervised learning approaches to many problems. As a result, unsupervised methods that learn directly from unlabeled data are increasingly important. For tasks related to unsupervised syntactic analysis, discrete generative models have dominated in recent ye... | [
"What datasets do they evaluate on?",
"Do they evaluate only on English datasets?",
"What is the invertibility condition?"
] | [
[
"",
""
],
[
"",
""
],
[
"The neural projector must be invertible.",
""
]
] |
Modelling the relationship between sequences is extremely significant in most retrieval or classification problems involving two sequences. Traditionally, in Siamese networks, Hadamard product or concatenation have been used to fuse two vector representations of two input sequences to form a final representation for ta... | [
"Do they show on which examples how conflict works better than attention?",
"Which neural architecture do they use as a base for their attention conflict mechanisms?",
"On which tasks do they test their conflict method?"
] | [
[
"",
""
],
[
"GRU-based encoder, interaction block, and classifier consisting of stacked fully-connected layers.",
""
],
[
"",
""
]
] |
Following developing news stories is imperative to making real-time decisions on important political and public safety matters. Given the abundance of media providers and languages, this endeavor is an extremely difficult task. As such, there is a strong demand for automatic clustering of news streams, so that they can... | [
"Do they use graphical models?",
"What are the sources of the datasets?",
"What metric is used for evaluation?"
] | [
[
"",
""
],
[
"",
""
],
[
"F1, precision, recall, accuracy",
"Precision, recall, F1, accuracy"
]
] |
Pretrained Language Models (PTLMs) such as BERT BIBREF1 have spearheaded advances on many NLP tasks. Usually, PTLMs are pretrained on unlabeled general-domain and/or mixed-domain text, such as Wikipedia, digital books or the Common Crawl corpus.When applying PTLMs to specific domains, it can be useful to domain-adapt t... | [
"Which eight NER tasks did they evaluate on?",
"What in-domain text did they use?"
] | [
[
"BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800",
"BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800"
],
[
"",
""
]
] |
Neural Machine Translation (NMT) has shown its effectiveness in translation tasks when NMT systems perform best in recent machine translation campaigns BIBREF0 , BIBREF1 . Compared to phrase-based Statistical Machine Translation (SMT) which is basically an ensemble of different features trained and tuned separately, NM... | [
"Does their framework automatically optimize for hyperparameters?",
"Does their framework always generate purely attention-based models?",
"Do they test their framework performance on commonly used language pairs, such as English-to-German?",
"Which languages do they test on for the under-resourced scenario?"... | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Automatically answering questions, especially in the open-domain setting (i.e., where minimal or no contextual knowledge is explicitly provided), requires bringing to bear considerable amount of background knowledge and reasoning abilities. For example, knowing the answers to the two questions in Figure FIGREF1 require... | [
"Are the automatically constructed datasets subject to quality control?",
"Do they focus on Reading Comprehension or multiple choice question answering?",
"After how many hops does accuracy decrease?",
"How do they control for annotation artificats?",
"Is WordNet useful for taxonomic reasoning for this task... | [
[
"",
""
],
[
"MULTIPLE CHOICE QUESTION ANSWERING",
""
],
[
"",
"one additional hop"
],
[
"",
""
],
[
"",
""
]
] |
This paper describes our approach and results for Task 2 of the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection BIBREF0 . The task is to generate an inflected word form given its lemma and the context in which it occurs.Morphological (re)inflection from context is of particular relevance to th... | [
"How do they perform multilingual training?",
"What languages are evaluated?",
"Does the model have attention?",
"What architecture does the decoder have?",
"What architecture does the encoder have?",
"What is MSD prediction?",
"What type of inflections are considered?"
] | [
[
"",
""
],
[
"German, English, Spanish, Finnish, French, Russian, Swedish.",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"The task of predicting MSD tags: V, PST, V.PCTP, PASS.",
""
],
[
"",
""
]
] |
Teaching machine to read and comprehend a given passage/paragraph and answer its corresponding questions is a challenging task. It is also one of the long-term goals of natural language understanding, and has important applications in e.g., building intelligent agents for conversation and customer service support. In a... | [
"Do they use attention?",
"What other models do they compare to?",
"What is the architecture of the span detector?"
] | [
[
"",
""
],
[
"SAN Baseline, BNA, DocQA, R.M-Reader, R.M-Reader+Verifier and DocQA+ELMo",
"BNA, DocQA, R.M-Reader, R.M-Reader + Verifier, DocQA + ELMo, R.M-Reader+Verifier+ELMo"
],
[
"",
""
]
] |
This research addresses the problem of representing the semantics of text documents in multi-lingual comparable corpora. We present a new approach to this problem, based on neural embeddings, and test it on the task of clustering texts into meaningful classes depending on their topics. The setting is unsupervised, mean... | [
"What evaluation metric do they use?"
] | [
[
"Accuracy",
""
]
] |
Many reinforcement learning algorithms are designed for relatively small discrete or continuous action spaces and so have trouble scaling. Text-adventure games—or interaction fictions—are simulations in which both an agents' state and action spaces are in textual natural language. An example of a one turn agent interac... | [
"What are the results from these proposed strategies?",
"What are the baselines?",
"What are the two new strategies?"
] | [
[
"Reward of 11.8 for the A2C-chained model, 41.8 for the KG-A2C-chained model, 40 for A2C-Explore and 44 for KG-A2C-Explore.",
""
],
[
"",
""
],
[
"",
""
]
] |
Chatbots such as dialog and question-answering systems have a long history in AI and natural language processing. Early such systems were mostly built using markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Recent ne... | [
"Do they report results only on English data?",
"How much better than the baseline is LiLi?",
"What baseline is used in the experiments?",
"In what way does LiLi imitate how humans acquire knowledge and perform inference during an interactive conversation?",
"What metrics are used to establish that this mak... | [
[
"",
""
],
[
"In case of Freebase knowledge base, LiLi model had better F1 score than the single model by 0.20 , 0.01, 0.159 for kwn, unk, and all test Rel type. The values for WordNet are 0.25, 0.1, 0.2. \n",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
... |
In practice, it is often difficult and costly to annotate sufficient training data for diverse application domains on-the-fly. We may have sufficient labeled data in an existing domain (called the source domain), but very few or no labeled data in a new domain (called the target domain). This issue has motivated resear... | [
"How many labels do the datasets have?",
"What is the architecture of the model?",
"What are the baseline methods?",
"What are the source and target domains?"
] | [
[
"719313",
"Book, Electronics, Beauty and Music each have 6000, IMDB 84919, Yelp 231163, Cell Phone 194792 and Baby 160792 labeled data."
],
[
"",
""
],
[
"",
""
],
[
"Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen",
""
]
] |
The focus of the word sense disambiguation (WSD) task is polysemy, i.e. words having several substantially different meanings. Two common examples are bank (riverside or financial institution) and bass (fish or musical instrument), but usually the meanings of a word are closely related, e.g. class may refer to: (a) a g... | [
"Did they use a crowdsourcing platform for annotations?",
"How do they deal with unknown distribution senses?"
] | [
[
"",
""
],
[
"The Näive-Bayes classifier is corrected so it is not biased to most frequent classes",
""
]
] |
Since its rise in 2013, the Islamic State of Iraq and Syria (ISIS) has utilized the Internet to spread its ideology, radicalize individuals, and recruit them to their cause. In comparison to other Islamic extremist groups, ISIS' use of technology was more sophisticated, voluminous, and targeted. For example, during ISI... | [
"Do they report results only on English data?",
"What conclusions do the authors draw from their finding that the emotional appeal of ISIS and Catholic materials are similar?",
"How id Depechemood trained?",
"How are similarities and differences between the texts from violent and non-violent religious groups ... | [
[
"",
""
],
[
"",
"By comparing scores for each word calculated using Depechemood dictionary and normalize emotional score for each article, they found Catholic and ISIS materials show similar scores"
],
[
"By multiplying crowd-annotated document-emotion matrix with emotion-word matrix. ... |
Automatically generating text to describe the content of images, also known as image captioning, is a multimodal task of considerable interest in both the computer vision and the NLP communities. Image captioning can be framed as a translation task from an image to a descriptive natural language statement. Many existin... | [
"Are the images from a specific domain?",
"Which datasets are used?",
"Which existing models are evaluated?",
"How is diversity measured?"
] | [
[
"",
""
],
[
"Existential (OneShape, MultiShapes), Spacial (TwoShapes, Multishapes), Quantification (Count, Ratio) datasets are generated from ShapeWorldICE",
"ShapeWorldICE datasets: OneShape, MultiShapes, TwoShapes, MultiShapes, Count, and Ratio"
],
[
"",
""
],
[
"",
"... |
Named entity recognition (NER) is a challenging problem in Natural Language Processing, and often serves as an important step for many popular applications, such as information extraction and question answering. NER requires phrases referring to entities in text be identified and assigned to particular entity types, th... | [
"What state-of-the-art deep neural network is used?",
"What boundary assembling method is used?",
"What are previous state of the art results?"
] | [
[
"",
""
],
[
"",
""
],
[
"Overall F1 score:\n- He and Sun (2017) 58.23\n- Peng and Dredze (2017) 58.99\n- Xu et al. (2018) 59.11",
"For Named entity the maximum precision was 66.67%, and the average 62.58%, same values for Recall was 55.97% and 50.33%, and for F1 57.14% and 55.64%. ... |
Reading Comprehension (RC) has become a central task in natural language processing, with great practical value in various industries. In recent years, many large-scale RC datasets in English BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 have nourished the development of numerous powerful and diverse RC... | [
"What is the model performance on target language reading comprehension?",
"What source-target language pairs were used in this work? ",
"What model is used as a baseline? ",
"what does the model learn in zero-shot setting?"
] | [
[
"",
""
],
[
"En-Fr, En-Zh, En-Jp, En-Kr, Zh-En, Zh-Fr, Zh-Jp, Zh-Kr to English, Chinese or Korean",
"",
""
],
[
"",
""
],
[
""
]
] |
Social media with abundant user-generated posts provide a rich platform for understanding events, opinions and preferences of groups and individuals. These insights are primarily hidden in unstructured forms of social media posts, such as in free-form text or images without tags. Named entity recognition (NER), the tas... | [
"Do they inspect their model to see if their model learned to associate image parts with words related to entities?",
"Does their NER model learn NER from both text and images?",
"Which types of named entities do they recognize?",
"Can named entities in SnapCaptions be discontigious?",
"How large is their M... | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
"10000"
]
] |
Large pre-trained language models BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 improved the state-of-the-art of various natural language understanding (NLU) tasks such as question answering (e.g., SQuAD; BIBREF5), natural language inference (e.g., MNLI; BIBREF6) as well as text classification BIBREF7. These models (i.e.... | [
"What is masked document generation?",
"Which of the three pretraining tasks is the most helpful?"
] | [
[
"A task for seq2seq model pra-training that recovers a masked document to its original form.",
""
],
[
"",
""
]
] |
Neural machine translation (NMT) has gained a lot of attention recently due to its substantial improvements in machine translation quality achieving state-of-the-art performance for several languages BIBREF0 , BIBREF1 , BIBREF2 . The core architecture of neural machine translation models is based on the general encoder... | [
"What useful information does attention capture?",
"What datasets are used?",
"In what cases is attention different from alignment?"
] | [
[
"",
"Alignment points of the POS tags."
],
[
"",
""
],
[
"For certain POS tags, e.g. VERB, PRON.",
""
]
] |
State-of-the-art automatic speech recognition (ASR) systems BIBREF0 have large model capacities and require significant quantities of training data to generalize. Labeling thousands of hours of audio, however, is expensive and time-consuming. A natural question to ask is how to achieve better generalization with fewer ... | [
"How do they calculate variance from the model outputs?",
"How much data samples do they start with before obtaining the initial model labels?",
"Which model do they use for end-to-end speech recognition?",
"Which dataset do they use?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
One of the most fundamental topics in natural language processing is how best to derive high-level representations from constituent parts, as natural language meanings are a function of their constituent parts. How best to construct a sentence representation from distributed word embeddings is an example domain of this... | [
"Which baselines did they compare against?"
] | [
[
"Various tree structured neural networks including variants of Tree-LSTM, Tree-based CNN, RNTN, and non-tree models including variants of LSTMs, CNNs, residual, and self-attention based networks",
"Sentence classification baselines: RNTN (Socher et al. 2013), AdaMC-RNTN (Dong et al. 2014), TE-RNTN (Qian e... |
Explanations of happenings in one's life, causal explanations, are an important topic of study in social, psychological, economic, and behavioral sciences. For example, psychologists have analyzed people's causal explanatory style BIBREF0 and found strong negative relationships with depression, passivity, and hostility... | [
"What baselines did they consider?",
"What types of social media did they consider?"
] | [
[
"",
"Linear SVM, RBF SVM, and Random Forest"
],
[
"",
""
]
] |
Task-oriented dialog systems have become ubiquitous, providing a means for billions of people to interact with computers using natural language. Moreover, the recent influx of platforms and tools such as Google's DialogFlow or Amazon's Lex for building and deploying such systems makes them even more accessible to vario... | [
"How was the dataset annotated?",
"Which classifiers are evaluated?",
"What is the size of this dataset?",
"Where does the data come from?"
] | [
[
"intents are annotated manually with guidance from queries collected using a scoping crowdsourcing task",
""
],
[
"",
""
],
[
"",
" 23,700 queries, including 22,500 in-scope queries covering 150 intents, which can be grouped into 10 general domains and 1,200 out-of-scope queries."
... |
Data imbalance is a common issue in a variety of NLP tasks such as tagging and machine reading comprehension. Table TABREF3 gives concrete examples: for the Named Entity Recognition (NER) task BIBREF2, BIBREF3, most tokens are backgrounds with tagging class $O$. Specifically, the number of tokens tagging class $O$ is 5... | [
"What are method improvements of F1 for paraphrase identification?",
"What are method's improvements of F1 for NER task for English and Chinese datasets?",
"What are method's improvements of F1 w.r.t. baseline BERT tagger for Chinese POS datasets?",
"How are weights dynamically adjusted?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
The task of generating natural language descriptions of structured data (such as tables) BIBREF2 , BIBREF3 , BIBREF4 has seen a growth in interest with the rise of sequence to sequence models that provide an easy way of encoding tables and generating text from them BIBREF0 , BIBREF1 , BIBREF5 , BIBREF6 .For text genera... | [
"Ngrams of which length are aligned using PARENT?",
"How many people participated in their evaluation study of table-to-text models?",
"By how much more does PARENT correlate with human judgements in comparison to other text generation metrics?"
] | [
[
"",
"Answer with content missing: (Parent subsections) combine precisions for n-gram orders 1-4"
],
[
"about 500",
""
],
[
"Best proposed metric has average correlation with human judgement of 0.913 and 0.846 compared to best compared metrics result of 0.758 and 0.829 on WikiBio and We... |
Natural Language Processing (NLP) has increasingly attracted the attention of the financial community. This trend can be explained by at least three major factors. The first factor refers to the business perspective. It is the economics of gaining competitive advantage using alternative sources of data and going beyond... | [
"Which stock market sector achieved the best performance?"
] | [
[
"Energy with accuracy of 0.538",
"Energy"
]
] |
Recurrent neural networks (RNNs), including gated variants such as the long short-term memory (LSTM) BIBREF0 have become the standard model architecture for deep learning approaches to sequence modeling tasks. RNNs repeatedly apply a function with trainable parameters to a hidden state. Recurrent layers can also be sta... | [
"What languages pairs are used in machine translation?",
"What sentiment classification dataset is used?",
"What pooling function is used?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Deep Neural Networks (DNN) have been widely employed in industry for solving various Natural Language Processing (NLP) tasks, such as text classification, sequence labeling, question answering, etc. However, when engineers apply DNN models to address specific NLP tasks, they often face the following challenges.The abov... | [
"Do they report results only on English?",
"What neural network modules are included in NeuronBlocks?",
"How do the authors evidence the claim that many engineers find it a big overhead to choose from multiple frameworks, models and optimization techniques?"
] | [
[
"",
""
],
[
"",
""
],
[
"By conducting a survey among engineers",
""
]
] |
Question classification (QC) deals with question analysis and question labeling based on the expected answer type. The goal of QC is to assign classes accurately to the questions based on expected answer. In modern system, there are two types of questions BIBREF0. One is Factoid question which is about providing concis... | [
"what datasets did they use?",
"what ml based approaches were compared?"
] | [
[
"Dataset of total 3500 questions from the Internet and other sources such as books of general knowledge questions, history, etc.",
"3500 questions collected from the internet and books."
],
[
"",
""
]
] |
Recently, neural machine translation (NMT) has gained popularity in the field of machine translation. The conventional encoder-decoder NMT proposed by Cho2014 uses two recurrent neural networks (RNN): one is an encoder, which encodes a source sequence into a fixed-length vector, and the other is a decoder, which decode... | [
"Is pre-training effective in their evaluation?",
"What parallel corpus did they use?"
] | [
[
"",
""
],
[
"",
""
]
] |
Single-document summarization is the task of generating a short summary for a given document. Ideally, the generated summaries should be fluent and coherent, and should faithfully maintain the most important information in the source document. purpleThis is a very challenging task, because it arguably requires an in-de... | [
"How much does their model outperform existing models?",
"What do they mean by global and local context?"
] | [
[
"Best proposed model result vs best previous result:\nArxiv dataset: Rouge 1 (43.62 vs 42.81), Rouge L (29.30 vs 31.80), Meteor (21.78 vs 21.35)\nPubmed dataset: Rouge 1 (44.85 vs 44.29), Rouge L (31.48 vs 35.21), Meteor (20.83 vs 20.56)",
"On arXiv dataset, the proposed model outperforms baselie model by... |
Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. In the Internet era, thanks to the mechanism of sharing in social networks, propaganda campaigns have the potential of reaching very large audiences BIBREF0, BIBREF1, BIBREF2.Propagandist news articles use specific techniqu... | [
"What are the 18 propaganda techniques?",
"What dataset was used?",
"What was the baseline for this task?"
] | [
[
"",
""
],
[
"",
""
],
[
"The baseline system for the SLC task is a very simple logistic regression classifier with default parameters. The baseline for the FLC task generates spans and selects one of the 18 techniques randomly.",
""
]
] |
Measures of semantic similarity and relatedness quantify the degree to which two concepts are similar (e.g., INLINEFORM0 – INLINEFORM1 ) or related (e.g., INLINEFORM2 – INLINEFORM3 ). Semantic similarity can be viewed as a special case of semantic relatedness – to be similar is one of many ways that a pair of concepts ... | [
"What is a second order co-ocurrence matrix?",
"How many humans participated?",
"What embedding techniques are explored in the paper?"
] | [
[
"",
"The matrix containing co-occurrences of the words which occur with the both words of every given pair of words."
],
[
"",
"16"
],
[
"",
""
]
] |
Single-relation factoid questions are the most common form of questions found in search query logs and community question answering websites BIBREF1 , BIBREF2 . A knowledge-base (KB) such as Freebase, DBpedia, or Wikidata can help answer such questions after users reformulate them as queries. For instance, the question... | [
"Do the authors also try the model on other datasets?",
"What word level and character level model baselines are used?"
] | [
[
"",
""
],
[
"None",
"Word-level Memory Neural Networks (MemNNs) proposed in Bordes et al. (2015)"
]
] |
The use of RNNs in the field of Statistical Machine Translation (SMT) has revolutionised the approaches to automated translation. As opposed to traditional shallow SMT models, which require a lot of memory to run, these neural translation models require only a small fraction of memory used, about 5% BIBREF0 . Also, neu... | [
"By how much do they improve the efficacy of the attention mechanism?",
"How were the human judgements assembled?"
] | [
[
"",
""
],
[
"50 human annotators ranked a random sample of 100 translations by Adequacy, Fluency and overall ranking on a 5-point scale.",
""
]
] |
Reordering in machine translation (MT) is a crucial process to get the correct translation output word order given an input source sentence, as word order reflects meaning. It remains a major challenge, especially for language pairs with a significant word order difference. Phrase-based MT systems BIBREF0 generally ado... | [
"Did they only experiment with one language pair?"
] | [
[
"",
""
]
] |
Named entity recognition (NER) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 is the process by which we identify text spans which mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language processing (NLP) applica... | [
"Which other approaches do they compare their model with?",
"What results do they achieve using their proposed approach?",
"How do they combine a deep learning model with a knowledge base?"
] | [
[
"Akbik et al. (2018), Link et al. (2012)",
"They compare to Akbik et al. (2018) and Link et al. (2012)."
],
[
"F-1 score on the OntoNotes is 88%, and it is 53% on Wiki (gold).",
""
],
[
"Entities from a deep learning model are linked to the related entities from a knowledge base by a l... |
Recent years have seen unprecedented progress for Natural Language Processing (NLP) on almost every NLP subtask. Even though low-resource settings have also been explored, this progress has overwhelmingly been observed in languages with significant data resources that can be leveraged to train deep neural networks. Low... | [
"What are the models used for the baseline of the three NLP tasks?",
"How is non-standard pronunciation identified?"
] | [
[
"",
"For speech synthesis, they build a speech clustergen statistical speech synthesizer BIBREF9. For speech recognition, they use Kaldi BIBREF11. For Machine Translation, they use a Transformer architecture from BIBREF15."
],
[
"",
"Original transcription was labeled with additional labels in... |
As observed by a recent article of Nature News BIBREF0 , “Wikipedia is among the most frequently visited websites in the world and one of the most popular places to tap into the world's scientific and medical information". Despite the huge amount of consultations, open issues still threaten a fully confident fruition o... | [
"Is it valid to presume a bad medical wikipedia article should not contain much domain-specific jargon?"
] | [
[
"",
""
]
] |
Dense word vectors (or embeddings) are a key component in modern NLP architectures for tasks such as sentiment analysis, parsing, and machine translation. These vectors can be learned by exploiting the distributional hypothesis BIBREF0, paraphrased by BIBREF1 as “a word is characterized by the company that it keeps”, u... | [
"What novel PMI variants are introduced?",
"What semantic and syntactic tasks are used as probes?",
"What are the disadvantages to clipping negative PMI?",
"Why are statistics from finite corpora unreliable?"
] | [
[
"clipped PMI; NNEGPMI",
""
],
[
"",
""
],
[
"It may lead to poor rare word representations and word analogies.",
""
],
[
"",
"A finite corpora may entirely omit rare word combinations"
]
] |
Typical speech-to-text translation systems pipeline automatic speech recognition (ASR) and machine translation (MT) BIBREF0 . But high-quality ASR requires hundreds of hours of transcribed audio, while high-quality MT requires millions of words of parallel text—resources available for only a tiny fraction of the world'... | [
"what is the domain of the corpus?",
"what challenges are identified?",
"what is the size of the speech corpus?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
The cognitive processes involved in human language comprehension are complex and only partially identified. According to the dual-stream model of speech comprehension BIBREF1 , sound waves are first converted to phoneme-like features and further processed by a ventral stream that maps those features onto words and sema... | [
"Which two pairs of ERPs from the literature benefit from joint training?",
"What datasets are used?"
] | [
[
"Answer with content missing: (Whole Method and Results sections) Self-paced reading times widely benefit ERP prediction, while eye-tracking data seems to have more limited benefit to just the ELAN, LAN, and PNP ERP components.\nSelect:\n- ELAN, LAN\n- PNP ERP",
""
],
[
"Answer with content missin... |
Part-of-speech tagging is now a classic task in natural language processing, for which many systems have been developed or adapted for a large variety of languages. Its aim is to associate each “word” with a morphosyntactic tag, whose granularity can range from a simple morphosyntactic category, or part-of-speech (here... | [
"which datasets did they experiment with?",
"which languages are explored?"
] | [
[
"Universal Dependencies v1.2 treebanks for the following 16 languages: Bulgarian, Croatian, Czech, Danish, English, French, German,\nIndonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish, and Swedish",
""
],
[
"",
""
]
] |
The polarization of actors' expressed preferences is a fundamental concern for studies of legislatures, court systems, and international politics BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Because preferences are unobservable, scholars must look for signals in the empirical world. Recent progress has been made in parliame... | [
"Do they use number of votes as an indicator of preference?",
"What does a node in the network approach repesent?",
"Which dataset do they use?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Social media platforms, particularly microblogging services such as Twitter, have become increasingly popular BIBREF0 as a means to express thoughts and opinions. Twitter users emit tweets about a wide variety of topics, which vary in the extent to which they reflect a user's personality, brand and interests. This obse... | [
"What kind of celebrities do they obtain tweets from?"
] | [
[
"Amitabh Bachchan, Ariana Grande, Barack Obama, Bill Gates, Donald Trump,\nEllen DeGeneres, J K Rowling, Jimmy Fallon, Justin Bieber, Kevin Durant, Kim Kardashian, Lady Gaga, LeBron James,Narendra Modi, Oprah Winfrey",
"Celebrities from varioius domains - Acting, Music, Politics, Business, TV, Author, Spo... |
Pretrained language models like Transformer-XL BIBREF1, ELMo BIBREF2 and BERT BIBREF3 have emerged as universal tools that capture a diverse range of linguistic and factual knowledge.Recently, BIBREF0 introduced LAMA (LAnguage Model Analysis) to investigate to what extent pretrained language models have the capacity to... | [
"How did they extend LAMA evaluation framework to focus on negation?"
] | [
[
"",
"Create the negated LAMA dataset and query the pretrained language models with both original LAMA and negated LAMA statements and compare their predictions."
]
] |
Performance appraisal (PA) is an important HR process, particularly for modern organizations that crucially depend on the skills and expertise of their workforce. The PA process enables an organization to periodically measure and evaluate every employee's performance. It also provides a mechanism to link the goals esta... | [
"What summarization algorithms did the authors experiment with?",
"What evaluation metrics were used for the summarization task?",
"What clustering algorithms were used?",
"What evaluation metrics are looked at for classification tasks?",
"What methods were used for sentence classification?",
"What is the... | [
[
"LSA, TextRank, LexRank and ILP-based summary.",
"LSA, TextRank, LexRank"
],
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"Logistic Regression, Multinomial Naive Bayes, Random Forest, AdaBoost, Linear SVM, SVM with ADWSK and Pattern-based",
"Logistic Regression, Mu... |
Neural machine translation (NMT) emerged in the last few years as a very successful paradigm BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . While NMT is generally more fluent than previous statistical systems, adequacy is still a major concern BIBREF4 : common mistakes include dropping source words and repeating words in the ... | [
"What are the language pairs explored in this paper?"
] | [
[
"",
""
]
] |
Deep learning, a sub-field of machine learning research, has driven the rapid progress in artificial intelligence research, leading to astonishing breakthroughs on long-standing problems in a plethora of fields such as computer vision and natural language processing. Tools powered by deep learning are changing the way ... | [
"Do they experiment with the toolkits?"
] | [
[
"",
""
]
] |
There is a recent spark of interest in the task of Question Answering (QA) over unstructured textual data, also referred to as Machine Reading Comprehension (MRC). This is mostly due to wide-spread success of advances in various facets of deep learning related research, such as novel architectures BIBREF0, BIBREF1 that... | [
"Have they made any attempt to correct MRC gold standards according to their findings? ",
"What features are absent from MRC gold standards that can result in potential lexical ambiguity?",
"What modern MRC gold standards are analyzed?",
"How does proposed qualitative annotation schema looks like?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
"MSMARCO, HOTPOTQA, RECORD, MULTIRC, NEWSQA, and DROP."
],
[
"",
""
]
] |
With the advent of social media platforms, increasing user base address their grievances over these platforms, in the form of complaints. According to BIBREF0, complaint is considered to be a basic speech act used to express negative mismatch between the expectation and reality. Transportation and its related logistics... | [
"How many tweets were collected?",
"What language is explored in this paper?"
] | [
[
"",
""
],
[
"",
"English language"
]
] |
Speech-to-Text translation (ST) is essential for a wide range of scenarios: for example in emergency calls, where agents have to respond emergent requests in a foreign language BIBREF0; or in online courses, where audiences and speakers use different languages BIBREF1. To tackle this problem, existing approaches can be... | [
"What are the baselines?",
"What is the attention module pretrained on?"
] | [
[
"",
"",
""
],
[
""
]
] |
Language model plays an important role in many natural language processing systems, such as in automatic speech recognition BIBREF0 , BIBREF1 and machine translation systems BIBREF2 , BIBREF3 . Recurrent neural network (RNN) based models BIBREF4 , BIBREF5 have recently shown success in language modeling, outperforming ... | [
"How long of dialog history is captured?"
] | [
[
"two previous turns",
""
]
] |
Question answering (QA) has drawn a lot of attention in the past few years. QA tasks on images BIBREF0 have been widely studied, but most focused on understanding text documents BIBREF1 . A representative dataset in text QA is SQuAD BIBREF1 , in which several end-to-end neural models have accomplished promising perform... | [
"What evaluation metrics were used?",
"What was the score of the proposed model?",
"What was the previous best model?",
"Which datasets did they use for evaluation?"
] | [
[
"",
""
],
[
"Best results authors obtain is EM 51.10 and F1 63.11",
"EM Score of 51.10"
],
[
"",
""
],
[
"",
""
]
] |
There have been many implementations of the word2vec model in either of the two architectures it provides: continuous skipgram and CBoW (BIBREF0). Similar distributed models of word or subword embeddings (or vector representations) find usage in sota, deep neural networks like BERT and its successors (BIBREF1, BIBREF2,... | [
"What hyperparameters are explored?",
"What Named Entity Recognition dataset is used?",
"What sentiment analysis dataset is used?",
"Do they test both skipgram and c-bow?"
] | [
[
"Dimension size, window size, architecture, algorithm, epochs, hidden dimension size, learning rate, loss function, optimizer algorithm.",
"Hyperparameters explored were: dimension size, window size, architecture, algorithm and epochs."
],
[
"",
""
],
[
"",
""
],
[
"",
... |
Table-to-text generation is an important and challenging task in natural language processing, which aims to produce the summarization of numerical table BIBREF0, BIBREF1. The related methods can be empirically divided into two categories, pipeline model and end-to-end model. The former consists of content selection, do... | [
"What is the state-of-the-art model for the task?",
"What is the strong baseline?"
] | [
[
"",
""
],
[
"",
""
]
] |
Public debates are a common platform for presenting and juxtaposing diverging viewpoints As opposed to monologues where speakers are limited to expressing their own beliefs, debates allow for participants to interactively attack their opponents' points while defending their own. The resulting flow of ideas is a key fea... | [
"what aspects of conversation flow do they look at?",
"what debates dataset was used?"
] | [
[
"The time devoted to self-coverage, opponent-coverage, and the number of adopted discussion points.",
""
],
[
"",
""
]
] |
Information extraction tasks have become very important not only in the Web, but also for in-house enterprise settings. One of the crucial steps towards understanding natural language is named entity recognition (NER), which aims to extract mentions of entity names in text. NER is necessary for many higher-level tasks ... | [
"what is the state of the art?",
"what standard dataset were used?"
] | [
[
"Babelfy, DBpedia Spotlight, Entityclassifier.eu, FOX, LingPipe MUC-7, NERD-ML, Stanford NER, TagMe 2"
],
[
"",
"",
""
]
] |
In this digital era, online discussions and interactions has become a vital part of daily life of which a huge part is covered by social media platforms like twitter, facebook, instagram etc. Similar to real life there exist anti-social elements in the cyberspace, who take advantage of the anonymous nature in cyber wor... | [
"Do they perform error analysis?",
"How do their results compare to state-of-the-art?",
"What is the Random Kitchen Sink approach?"
] | [
[
"",
""
],
[
"",
""
],
[
"Random Kitchen Sink method uses a kernel function to map data vectors to a space where linear separation is possible.",
""
]
] |
We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs: English INLINEFORM0 Czech, English INLINEFORM1 German, English INLINEFORM2 Romanian and English INLINEFORM3 Russian. Our systems are based on an attentional encoder-decoder BIBREF0 , using BPE su... | [
"what are the baseline systems?"
] | [
[
"",
""
]
] |
Equations are an important part of scientific articles, but many existing machine learning methods do not easily handle them. They are challenging to work with because each is unique or nearly unique; most equations occur only once. An automatic understanding of equations, however, would significantly benefit methods f... | [
"What word embeddings do they test?",
"How do they define similar equations?"
] | [
[
"",
""
],
[
"By using Euclidean distance computed between the context vector representations of the equations",
""
]
] |
Automated, or robotic, journalism aims at news generation from structured data sources, either as the final product or as a draft for subsequent post-editing. At present, automated journalism typically focuses on domains such as sports, finance and similar statistics-based reporting, where there is a commercial product... | [
"What evaluation criteria and metrics were used to evaluate the generated text?"
] | [
[
"",
""
]
] |
In recent years, there has been a movement to leverage social medial data to detect, estimate, and track the change in prevalence of disease. For example, eating disorders in Spanish language Twitter tweets BIBREF0 and influenza surveillance BIBREF1 . More recently, social media has been leveraged to monitor social ris... | [
"Do they evaluate only on English datasets?",
"What are the three steps to feature elimination?",
"How is the dataset annotated?",
"What dataset is used for this study?"
] | [
[
"",
""
],
[
"",
"reduced the dataset by eliminating features, apply feature selection to select highest ranked features to train and test the model and rank the performance of incrementally adding features."
],
[
"",
"The annotations are based on evidence of depression and further ... |
Medical search engines are an essential component for many online medical applications, such as online diagnosis systems and medical document databases. A typical online diagnosis system, for instance, relies on a medical search engine. The search engine takes as input a user query that describes some symptoms and then... | [
"what were their performance results?",
"where did they obtain the annotated clinical notes from?"
] | [
[
"",
""
],
[
"",
""
]
] |
Conventional large-vocabulary continuous speech recognition (LVCSR) systems typically perform multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units such as sub-words (phonemes), words, and strings of words (sentences). Such systems basically consist of several sub-... | [
"Which architecture do they use for the encoder and decoder?",
"How does their decoder generate text?",
"Which dataset do they use?"
] | [
[
"",
"In encoder they use convolutional, NIN and bidirectional LSTM layers and in decoder they use unidirectional LSTM "
],
[
"",
"Decoder predicts the sequence of phoneme or grapheme at each time based on the previous output and context information with a beam search strategy"
],
[
"",... |
Over the past few years, generating text from images and videos has gained a lot of attention in the Computer Vision and Natural Language Processing communities and several related tasks have been proposed, such as image labeling, image and video description and visual question answering. In particular, prominent resul... | [
"What model is used to encode the images?",
"How is the sequential nature of the story captured?",
"Is the position in the sequence part of the input?",
"Do the decoder LSTMs all have the same weights?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
Knowledge graphs BIBREF0 enable structured access to world knowledge and form a key component of several applications like search engines, question answering systems and conversational assistants. Knowledge graphs are typically interpreted as comprising of discrete triples of the form (entityA, relationX, entityB) thus... | [
"Is fine-tuning required to incorporate these embeddings into existing models?",
"How are meaningful chains in the graph selected?"
] | [
[
"",
""
],
[
"",
""
]
] |
This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/.Sentence pair modeling is a fundamental technique underlying many NLP tasks, including the following:Traditionally, researchers had to develop different methods specific fo... | [
"Do the authors also analyze transformer-based architectures?"
] | [
[
"",
""
]
] |
Earlier studies on stock market prediction are based on the historical stock prices. Later studies have debunked the approach of predicting stock market movements using historical prices. Stock market prices are largely fluctuating. The efficient market hypothesis (EMH) states that financial market movements depend on ... | [
"Do they remove seasonality from the time series?",
"What is the dimension of the embeddings?",
"What dataset is used to train the model?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
"Collected tweets and opening and closing stock prices of Microsoft."
]
] |
Other than encoder-only pretrained transformer architectures BIBREF2, BIBREF3, BIBREF4, encoder–decoder style pretrained transformers BIBREF0, BIBREF5 have been proven to be effective in text generation tasks as well as comprehension tasks. This paper describes our submission to the commonsense reasoning task leaderboa... | [
"What is the previous state of the art?"
] | [
[
"",
""
]
] |
The vast amounts of data collected by healthcare providers in conjunction with modern data analytics techniques present a unique opportunity to improve health service provision and the quality and safety of medical care for patient benefit BIBREF0 . Much of the recent research in this area has been on personalised medi... | [
"Which text embedding methodologies are used?"
] | [
[
"",
""
]
] |
[0]leftmargin=* [0]leftmargin=*Automatic systems have had a significant and beneficial impact on all walks of human life. So much so that it is easy to overlook their potential to benefit society by promoting equity, diversity, and fairness. For example, machines do not take bribes to do their jobs, they can determine ... | [
"Which race and gender are given higher sentiment intensity predictions?",
"What criteria are used to select the 8,640 English sentences?"
] | [
[
"Females are given higher sentiment intensity when predicting anger, joy or valence, but males are given higher sentiment intensity when predicting fear.\nAfrican American names are given higher score on the tasks of anger, fear, and sadness intensity prediction, but European American names are given higher... |
Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words. Hybrid systems ... | [
"what were the baselines?",
"what competitive results did they obtain?"
] | [
[
"",
"LF-MMI Attention\nSeq2Seq \nRNN-T \nChar E2E LF-MMI \nPhone E2E LF-MMI \nCTC + Gram-CTC"
],
[
"In case of read speech datasets, their best model got the highest nov93 score of 16.1 and the highest nov92 score of 13.3.\nIn case of Conversational Speech, their best model got the highest SWB of... |
Understanding passenger intents from spoken interactions and car's vision (both inside and outside the vehicle) are important building blocks towards developing contextual dialog systems for natural interactions in autonomous vehicles (AV). In this study, we continued exploring AMIE (Automated-vehicle Multimodal In-cab... | [
"By how much is performance improved with multimodality?",
"Is collected multimodal in cabin dataset public?"
] | [
[
"by 2.3-6.8 points in f1 score for intent recognition and 0.8-3.5 for slot filling",
"F1 score increased from 0.89 to 0.92"
],
[
"",
""
]
] |
Informal speech is different from formal speech, especially in Vietnamese due to many conjunctive words in this language. Building an ASR model to handle such kind of speech is particularly difficult due to the lack of training data and also cost for data collection. There are two components of an ASR system that contr... | [
"What is the performance reported for the best models in the VLSP 2018 and VLSP 2019 challenges?",
"Is the model tested against any baseline?",
"What is the language model combination technique used in the paper?",
"What are the deep learning architectures used in the task?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"",
""
]
] |
The ability of semantic reasoning is essential for advanced natural language understanding (NLU) systems. Many NLU tasks that take sentence pairs as input, such as natural language inference (NLI) and machine reading comprehension (MRC), heavily rely on the ability of sophisticated semantic reasoning. For instance, the... | [
"How much is performance improved on NLI?",
"Do they train their model starting from a checkpoint?",
"What BERT model do they test?"
] | [
[
"",
"The average score improved by 1.4 points over the previous best result."
],
[
"",
""
],
[
"",
""
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.