uniq_id stringlengths 36 36 | messages stringlengths 2.65k 180k | answers listlengths 1 1 | num_tokens int64 507 32.7k | task stringclasses 7
values |
|---|---|---|---|---|
0513ce97-478d-42df-a62e-74dd205662d1 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What baseline models are used?\n\nIntroduction\nAttempts toward constructing human-like dialogue agents have met significant difficulties, such as maintaining conversation consistency BIBREF0. This is largely due to inabilities of dia... | [
"Kvmemnn, Feed Yourself, Poly-encoder, BERT bi-ranker"
] | 7,235 | qasper |
16d2adb5-5777-4547-ac70-fb6652d0ee20 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is the reported agreement for the annotation?\n\nIntroduction\nCancer is one of the leading causes of death in the world, with over 80,000 deaths registered in Canada in 2017 (Canadian Cancer Statistics 2017). A computer-aided sy... | [
"Unanswerable"
] | 2,758 | qasper |
2063e9ae-9bc3-439e-8a9a-97713242690e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How do their results compare to state-of-the-art?\n\nIntroduction\nIn 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, ... | [
"Unanswerable"
] | 3,371 | qasper |
224e3d5e-7829-4e51-a136-657e0aa4acd2 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What are the user-defined keywords?\n\nIntroduction\nThe development of online peer-to-peer markets in the 1990s, galvanized by the launch of sites like eBay, fundamentally shifted the way buyers and sellers could connect [4]. These n... | [
"Words that a user wants them to appear in the generated output."
] | 3,506 | qasper |
2c3c12e9-8335-4f01-ba41-deb318627df4 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Do the decoder LSTMs all have the same weights?\n\nIntroduction\nOver 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 seve... | [
"No"
] | 1,777 | qasper |
3894b64d-00e2-42a3-b415-3f60c7daf4f7 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What off-the-shelf reward learning algorithm from RL for joint data manipulation learning and model training is adapted?\n\nIntroduction\nThe performance of machines often crucially depend on the amount and quality of the data used fo... | [
" reward learning algorithm BIBREF7"
] | 6,950 | qasper |
3b837f03-90b4-4439-9b0a-5eaae67cf038 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What languages pairs are used in machine translation?\n\nIntroduction\nRecurrent neural networks (RNNs), including gated variants such as the long short-term memory (LSTM) BIBREF0 have become the standard model architecture for deep l... | [
"German–English"
] | 4,699 | qasper |
3eb5dacd-9135-404f-a9bc-b8c595b7ff4a | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How large is the corpus?\n\nIntroduction\nThe automatic processing of medical texts and documents plays an increasingly important role in the recent development of the digital health area. To enable dedicated Natural Language Processi... | [
"The corpus comprises 8,275 sentences and 167,739 words in total."
] | 3,587 | qasper |
409fbf06-1589-40b7-975d-f38130ef6322 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What future possible improvements are listed?\n\nIntroduction\nKnowledge and/or data is often modeled in a structure, such as indexes, tables, key-value pairs, or triplets. These data, by their nature (e.g., raw data or long time-seri... | [
"rther constrain the model on the data structure in order to prevent inaccurate of even contradictory descriptions"
] | 7,007 | qasper |
52cd48eb-0798-480b-9a25-0b95c8a5cb3b | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What are the 12 languages covered?\n\nIntroduction\nThe lack of annotated training and evaluation data for many tasks and domains hinders the development of computational models for the majority of the world's languages BIBREF0, BIBRE... | [
"Chinese Mandarin, Welsh, English, Estonian, Finnish, French, Hebrew, Polish, Russian, Spanish, Kiswahili, Yue Chinese"
] | 21,904 | qasper |
588982ec-b085-4ad3-b7f0-bfe19f9d3f0d | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What word level and character level model baselines are used?\n\nIntroduction\nSingle-relation factoid questions are the most common form of questions found in search query logs and community question answering websites BIBREF1 , BIBR... | [
"Word-level Memory Neural Networks (MemNNs) proposed in Bordes et al. (2015)"
] | 5,343 | qasper |
7191933e-ec97-4922-a6d1-48c2d4e9c35f | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How many humans participated?\n\nIntroduction\nMeasures of semantic similarity and relatedness quantify the degree to which two concepts are similar (e.g., INLINEFORM0 – INLINEFORM1 ) or related (e.g., INLINEFORM2 – INLINEFORM3 ). Sem... | [
"Unanswerable"
] | 5,721 | qasper |
71c03474-7710-46e0-980f-c7f604a0db8e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is the definition of offensive language?\n\nIntroduction\nOffensive content has become pervasive in social media and a reason of concern for government organizations, online communities, and social media platforms. One of the mos... | [
" Most prior work focuses on a different aspect of offensive language such as abusive language BIBREF0 , BIBREF1 , (cyber-)aggression BIBREF2 , (cyber-)bullying BIBREF3 , BIBREF4 , toxic comments INLINEFORM0 , hate speech BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , and offensive language BIBREF11 .... | 3,085 | qasper |
7dba2edd-5d50-40c2-bfb0-84729075e7dc | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What results do they achieve using their proposed approach?\n\nIntroduction\nNamed entity recognition (NER) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 is the process by which we identify text spans which mention named entities, and to clas... | [
"F-1 score on the OntoNotes is 88%, and it is 53% on Wiki (gold)."
] | 3,158 | qasper |
852a54a7-51ec-4136-ba36-9e8fb93acecb | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which machine learning models do they explore?\n\nIntroduction\nNamed Entity Recognition (NER) is a foremost NLP task to label each atomic elements of a sentence into specific categories like \"PERSON\", \"LOCATION\", \"ORGANIZATION\"... | [
"BiLSTM, BiLSTM-CNN, BiLSTM-CRF, BiLSTM-CNN-CRF"
] | 4,287 | qasper |
89f783ad-17cd-4994-bfc9-2c7c5d8c357e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What were their best results on the benchmark datasets?\n\nIntroduction\nIn the field of natural language processing (NLP), the most prevalent neural approach to obtaining sentence representations is to use recurrent neural networks (... | [
"In SNLI, our best model achieves the new state-of-the-art accuracy of 87.0%, we can see that our models outperform other models by large margin, achieving the new state of the art., Our models achieve the new state-of-the-art accuracy on SST-2 and competitive accuracy on SST-5"
] | 4,503 | qasper |
a0ea191d-7f49-44ec-b4ea-d9799ed4c421 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What neural network modules are included in NeuronBlocks?\n\nIntroduction\nDeep Neural Networks (DNN) have been widely employed in industry for solving various Natural Language Processing (NLP) tasks, such as text classification, sequ... | [
"Embedding Layer, Neural Network Layers, Loss Function, Metrics"
] | 2,357 | qasper |
aab3b46b-59a4-4ee6-b75a-c284c1b29e4a | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What natural language(s) are the recipes written in?\n\nIntroduction\nIn the kitchen, we increasingly rely on instructions from cooking websites: recipes. A cook with a predilection for Asian cuisine may wish to prepare chicken curry,... | [
"English"
] | 3,953 | qasper |
b181a536-e24f-4675-b54e-99e1913a984b | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"which datasets did they experiment with?\n\nIntroduction\nAlthough Neural Machine Translation (NMT) has dominated recent research on translation tasks BIBREF0, BIBREF1, BIBREF2, NMT heavily relies on large-scale parallel data, resulti... | [
"Europarl, MultiUN"
] | 5,432 | qasper |
ba1dc6e1-93a9-430d-8fe1-24e39b292b21 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What baselines were used in this work?\n\nIntroduction\nSocial media platforms have made the spreading of fake news easier, faster as well as able to reach a wider audience. Social media offer another feature which is the anonymity fo... | [
"LR + Bag-of-words, Tweet2vec, LR + All Features (tweet-level), LR + All Features (chunk-level), FacTweet (tweet-level), Top-$k$ replies, likes, or re-tweets"
] | 2,884 | qasper |
c651d9e9-d628-409c-9257-ebd6c7ed9c8e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Do they show on which examples how conflict works better than attention?\n\nIntroduction\nModelling the relationship between sequences is extremely significant in most retrieval or classification problems involving two sequences. Trad... | [
"Yes"
] | 3,185 | qasper |
e79a6c53-687e-42f5-a4b2-81ce46e14f55 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What baseline is used?\n\nIntroduction\nPeople are increasingly using social networking platforms such as Twitter, Facebook, YouTube, etc. to communicate their opinions and share information. Although the interactions among users on t... | [
"Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10"
] | 5,695 | qasper |
fbeecee0-e1dc-4533-a67c-29682e4f4556 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Do they employ their indexing-based method to create a sample of a QA Wikipedia dataset?\n\nIntroduction\nQuestion answering (QA) has been a blooming research field for the last decade. Selection-based QA implies a family of tasks tha... | [
"No"
] | 2,612 | qasper |
ffbd3041-d724-4f1d-8565-41c92594f748 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Does this method help in sentiment classification task improvement?\n\nIntroduction\nThe NLP community is revisiting the role of linguistic structure in applications with the advent of contextual word representations (cwrs) derived fr... | [
"No"
] | 3,683 | qasper |
0015838e-54ed-47e5-a302-c572b4b02e73 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How does their decoder generate text?\n\nIntroduction\nConventional large-vocabulary continuous speech recognition (LVCSR) systems typically perform multi-level pattern recognition tasks that map the acoustic speech waveform into a hi... | [
"Decoder predicts the sequence of phoneme or grapheme at each time based on the previous output and context information with a beam search strategy"
] | 3,653 | qasper |
06ec5a8c-2894-405c-9809-9f4109669930 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What cognitive features are used?\n\nIntroduction\nSarcasm is an intensive, indirect and complex construct that is often intended to express contempt or ridicule . Sarcasm, in speech, is multi-modal, involving tone, body-language and ... | [
"Readability (RED), Number of Words (LEN), Avg. Fixation Duration (FDUR), Avg. Fixation Count (FC), Avg. Saccade Length (SL), Regression Count (REG), Skip count (SKIP), Count of regressions from second half\nto first half of the sentence (RSF), Largest Regression Position (LREG), Edge density of the saliency gaze... | 4,864 | qasper |
074a5a95-c5d7-42b8-84c0-dd9e9f5287a7 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How many different types of entities exist in the dataset?\n\nIntroduction\nNamed Entity Recognition (NER) is a foremost NLP task to label each atomic elements of a sentence into specific categories like \"PERSON\", \"LOCATION\", \"OR... | [
"OurNepali contains 3 different types of entities, ILPRL contains 4 different types of entities"
] | 4,290 | qasper |
08e556ea-eef4-45d3-9aa1-50d44ceb15ba | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which datasets are employed for South African languages LID?\n\nIntroduction\nAccurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines. If the language of a piece... | [
"DSL 2015, DSL 2017, JW300 parallel corpus , NCHLT text corpora"
] | 2,613 | qasper |
09c61b27-a346-404b-9d0c-1306b7c561b1 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What boundary assembling method is used?\n\nIntroduction\nNamed 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... | [
"backward greedy search over each sentence's label sequence to identify word boundaries"
] | 2,380 | qasper |
0dc1a348-f152-4dda-9d34-3974d89e7a21 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is the improvement in performance for Estonian in the NER task?\n\nIntroduction\nWord embeddings are representations of words in numerical form, as vectors of typically several hundred dimensions. The vectors are used as an input... | [
"0.05 F1"
] | 4,621 | qasper |
14eac31a-04ed-4536-875b-58cae23f49a8 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which languages do they test on for the under-resourced scenario?\n\nIntroduction\nNeural Machine Translation (NMT) has shown its effectiveness in translation tasks when NMT systems perform best in recent machine translation campaigns... | [
"English, German"
] | 5,752 | qasper |
20e78771-eea4-4f8c-9262-89603669bc3c | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"what language pairs are explored?\n\nIntroduction\nAlthough Neural Machine Translation (NMT) has dominated recent research on translation tasks BIBREF0, BIBREF1, BIBREF2, NMT heavily relies on large-scale parallel data, resulting in p... | [
"French-English-Spanish (Fr-En-Es), German-English-French (De-En-Fr) and Romanian-English-German (Ro-En-De), Arabic (Ar), Spanish (Es), and Russian (Ru), and mutual translation between themselves constitutes six zero-shot translation"
] | 5,431 | qasper |
2932358b-a91b-4679-8ef4-5848ffd45b54 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How do they deal with unknown distribution senses?\n\nIntroduction\nThe focus of the word sense disambiguation (WSD) task is polysemy, i.e. words having several substantially different meanings. Two common examples are bank (riverside... | [
"Bayesian classifier has been modified, removing the bias towards frequent labels in the training data"
] | 4,989 | qasper |
2b416aa4-4ac2-44b1-8d44-a66153c63ce3 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What semantic and syntactic tasks are used as probes?\n\nIntroduction\nDense word vectors (or embeddings) are a key component in modern NLP architectures for tasks such as sentiment analysis, parsing, and machine translation. These ve... | [
"SimLex, Rare Word, Google Semantic, Semantic Textual Similarity, Word Content (WC) probing, Google Syntactic analogies, Depth, Top Constituent, part-of-speech (POS) tagging"
] | 3,416 | qasper |
3063a1b1-f2c1-4f75-b7b8-7c21acea260b | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"In the proposed metric, how is content relevance measured?\n\nIntroduction\nAutomatic text summarization has been an active research area in natural language processing for several decades. To compare and evaluate the performance of d... | [
"The content relevance between the candidate summary and the human summary is evaluated using information retrieval - using the summaries as search queries and compare the overlaps of the retrieved results. "
] | 6,267 | qasper |
30a20e0a-7d9a-437d-a04b-199e01deec9d | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"which existing strategies are compared?\n\nIntroduction\nNatural Language Processing (NLP) models are shown to capture unwanted biases and stereotypes found in the training data which raise concerns about socioeconomic, ethnic and gen... | [
"CDA, REG"
] | 3,223 | qasper |
34bc132a-4853-4256-af27-d1cf8a2e8d58 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What experiments are used to demonstrate the benefits of this approach?\n\nIntroduction\nWord embeddings BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 are unsupervised learning methods for capturing latent semant... | [
"Calculate test log-likelihood on the three considered datasets"
] | 4,298 | qasper |
3b69ebc8-a7ba-4e9e-9544-fd77ff3e1f48 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"what dataset is used in this paper?\n\nIntroduction\nUsers of photo-sharing websites such as Flickr often provide short textual descriptions in the form of tags to help others find the images. With the availability of GPS systems in c... | [
" the same datasets as BIBREF7"
] | 6,183 | qasper |
3b6f5289-33f5-4efb-99f7-633eee5551d0 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Is Arabic one of the 11 languages in CoVost?\n\nIntroduction\nEnd-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automati... | [
"No"
] | 3,604 | qasper |
43ef47e2-59f9-42d3-90ad-e5254625dcee | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What different correlations result when using different variants of ROUGE scores?\n\nIntroduction\nAutomatic text summarization has been an active research area in natural language processing for several decades. To compare and evalua... | [
"we observe that many variants of Rouge scores do not have high correlations with human pyramid scores"
] | 6,269 | qasper |
4edb9d73-a26c-4060-bdfd-c64e39823fd5 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How is the dataset of hashtags sourced?\n\nIntroduction\nA hashtag is a keyphrase represented as a sequence of alphanumeric characters plus underscore, preceded by the # symbol. Hashtags play a central role in online communication by ... | [
"1,268 randomly selected tweets in the Stanford Sentiment Analysis Dataset BIBREF36, all 12,594 unique English hashtags and their associated tweets from the same Stanford dataset"
] | 5,414 | qasper |
62f331b3-b522-4034-b1f9-a511867a5f8f | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"which datasets did they experiment with?\n\nIntroduction\nPart-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 ... | [
"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"
] | 3,760 | qasper |
66cb76f0-f8c4-43e5-b7e9-1aac7b97e1fd | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Did they use a crowdsourcing platform?\n\nIntroduction\nUnderstanding what a question is asking is one of the first steps that humans use to work towards an answer. In the context of question answering, question classification allows ... | [
"No"
] | 8,028 | qasper |
6cf804a1-0dba-409a-bde7-88b38fde3351 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What subtasks did they participate in?\n\nIntroduction\nUnderstanding the emotions expressed in a text or message is of high relevance nowadays. Companies are interested in this to get an understanding of the sentiment of their curren... | [
"Answer with content missing: (Subscript 1: \"We did not participate in subtask 5 (E-c)\") Authors participated in EI-Reg, EI-Oc, V-Reg and V-Oc subtasks."
] | 3,100 | qasper |
7099da5a-0e8b-45c5-9595-87b7da38165c | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which types of named entities do they recognize?\n\nIntroduction\nSocial media with abundant user-generated posts provide a rich platform for understanding events, opinions and preferences of groups and individuals. These insights are... | [
"PER, LOC, ORG, MISC"
] | 5,441 | qasper |
817318c6-a240-42f5-bc83-c0b4a995f5ce | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How are the different components of the model trained? Is it trained end-to-end?\n\nIntroduction\nText summarization generates summaries from input documents while keeping salient information. It is an important task and can be applie... | [
"the model can be trained end-to-end"
] | 5,696 | qasper |
8cf2baa8-f399-4e09-b30b-1f950aaa8ef7 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What evaluation criteria and metrics were used to evaluate the generated text?\n\nIntroduction\nAutomated, or robotic, journalism aims at news generation from structured data sources, either as the final product or as a draft for subs... | [
"BLEU, NIST, METEOR, ROUGE-L, CIDEr"
] | 5,972 | qasper |
9e1de53a-83f3-4d1a-8550-156c94876f0e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How did they determine fake news tweets?\n\n10pt\n1.10pt\n[ Characterizing Political Fake News in Twitter by its Meta-DataJulio Amador Díaz LópezAxel Oehmichen Miguel Molina-Solana( j.amador, axelfrancois.oehmichen11, mmolinas@imperia... | [
"an expert annotator determined if the tweet fell under a specific category"
] | 3,928 | qasper |
a01d1bab-710f-4abd-a7b5-5d45d805ae70 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What types of subwords do they incorporate in their model?\n\nIntroduction\nLow dimensional word representations (embeddings) have become a key component in modern NLP systems for language modeling, parsing, sentiment classification, ... | [
"simple n-grams (like fastText) and unsupervised morphemes"
] | 3,208 | qasper |
be05757b-7c7c-4545-b701-70f6ec3988fb | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"how many articles are in the dataset?\n\nIntroduction\nThis work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/\nRecent years have seen a surge ... | [
"244"
] | 2,275 | qasper |
c2933e34-1ff4-4fdd-a03d-eb076c99f038 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which hand-crafted features are combined with word2vec?\n\nIntroduction\nSentiment analysis has recently been one of the hottest topics in natural language processing (NLP). It is used to identify and categorise opinions expressed by ... | [
"three hand-crafted polarity scores, which are minimum, mean, and maximum polarity scores"
] | 5,008 | qasper |
ccbf38b2-04d3-4a9f-a0b6-c0fd6b28bbd5 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What are the other algorithms tested?\n\nIntroduction\nDuring 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 capabiliti... | [
"As the simplest baseline, a sensitive data recogniser and classifier, Conditional Random Fields (CRF), spaCy "
] | 6,150 | qasper |
dc0220f3-7db9-4648-8944-2e2dcfb5bc1b | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What criteria are used to select the 8,640 English sentences?\n\nIntroduction\n[0]leftmargin=* [0]leftmargin=*\nAutomatic systems have had a significant and beneficial impact on all walks of human life. So much so that it is easy to o... | [
"generated with the various combinations of INLINEFORM4 person INLINEFORM5 and INLINEFORM6 emotion word INLINEFORM7 values across the eleven templates, differ only in one word corresponding to gender or race"
] | 6,032 | qasper |
077e5942-3c61-4acd-aa66-9a75685f34f0 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"how small of a dataset did they train on?\n\nIntroduction\nRecently, deep neural network has been widely employed in various recognition tasks. Increasing the depth of neural network is a effective way to improve the performance, and ... | [
"1000 hours data"
] | 5,554 | qasper |
0fcbd84a-8e63-42e6-85ac-1138a0fb86bc | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What discourse relations does it work best/worst for?\n\nIntroduction\nPDTB-style discourse relations, mostly defined between two adjacent text spans (i.e., discourse units, either clauses or sentences), specify how two discourse unit... | [
"explicit discourse relations"
] | 5,574 | qasper |
16300ab3-7af8-4712-8976-d2015227847b | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What are the baselines?\n\nIntroduction\nSpeech-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 o... | [
"Vanilla ST baseline, Pre-training baselines, Multi-task baselines, Many-to-many+pre-training, Triangle+pre-train"
] | 7,491 | qasper |
1beb2cbf-a61e-45fc-a1fc-a6b64680b0eb | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Do the authors report results only on English data?\n\nIntroduction\nAutomatic text summarization has been an active research area in natural language processing for several decades. To compare and evaluate the performance of differen... | [
"Yes"
] | 6,266 | qasper |
34bd3628-9382-41a6-9a95-59b0911bd062 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What metrics are used to establish that this makes chatbots more knowledgeable and better at learning and conversation? \n\nIntroduction\nChatbots such as dialog and question-answering systems have a long history in AI and natural lan... | [
"Coverage, Avg. MCC and avg. +ve F1 score"
] | 8,591 | qasper |
46550249-41f9-48cd-a47f-aa7bb9a796d8 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which classification algorithm do they use for s2sL?\n\nIntroduction\nDeep neural networks (DNNs), in particular convolutional and recurrent neural networks, with huge architectures have been proven successful in wide range of tasks i... | [
"MLP"
] | 4,148 | qasper |
482124d5-520c-481e-a2b2-0a28d5c48d57 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is the CORD-19 dataset?\n\nIntroduction\nCoronavirus disease 2019 (COVID-19) is an infectious disease that has affected more than one million individuals all over the world and caused more than 55,000 deaths, as of April 3 in 202... | [
"contains over 45,000 scholarly articles, including over 33,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses"
] | 2,991 | qasper |
5ada56d3-1849-4741-84e1-cfdae4cef8f6 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is the size of the parallel corpus used to train the model constraints?\n\nIntroduction\nPrepositional Phrase (PP) attachment disambiguation is an important problem in NLP, for it often gives rise to incorrect parse trees . Stati... | [
"1500 sentences"
] | 2,956 | qasper |
651b2a19-d913-4cbb-800e-592b57e018ea | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How do they calculate variance from the model outputs?\n\nIntroduction\nState-of-the-art automatic speech recognition (ASR) systems BIBREF0 have large model capacities and require significant quantities of training data to generalize.... | [
" Fisher Information Ratio"
] | 2,235 | qasper |
67259107-2547-4d0f-8a02-0412dd32888c | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How does their simple voting scheme work?\n\nIntroduction\nRelation classification is the task of assigning sentences with two marked entities to a predefined set of relations. The sentence “We poured the <e1>milk</e1> into the <e2>pu... | [
"we apply several CNN and RNN models presented in Tables TABREF12 and TABREF14 and predict the class with the most votes, In case of a tie, we pick one of the most frequent classes randomly"
] | 3,317 | qasper |
71319a48-b804-4b62-8317-8185d84a867e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Is WordNet useful for taxonomic reasoning for this task?\n\nIntroduction\nAutomatically answering questions, especially in the open-domain setting (i.e., where minimal or no contextual knowledge is explicitly provided), requires bring... | [
"Unanswerable"
] | 9,204 | qasper |
85242b72-9f34-463b-9c8a-0b5f14a9c6ae | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What state-of-the-art compression techniques were used in the comparison?\n\nIntroduction\nRecently, contextual-aware language models such as ELMo BIBREF0, GPT BIBREF1, BERT BIBREF2 and XLNet BIBREF3 have shown to greatly outperform t... | [
"NoKD, PKD, BERTBASE teacher model"
] | 4,883 | qasper |
a355044c-dcbc-438a-a6c0-84508891bcd5 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What dataset is used?\n\nIntroduction\nRendering natural language descriptions from structured data is required in a wide variety of commercial applications such as generating descriptions of products, hotels, furniture, etc., from a ... | [
"English WIKIBIO, French WIKIBIO , German WIKIBIO "
] | 5,865 | qasper |
af988a26-0641-4797-a8e0-3783a91d4363 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What news article sources are used?\n\nIntroduction\nWikipedia is the largest source of open and collaboratively curated knowledge in the world. Introduced in 2001, it has evolved into a reference work with around 5m pages for the Eng... | [
" the news external references in Wikipedia"
] | 10,237 | qasper |
c789a6e6-ae73-47dd-8917-724a19dd9400 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What data is used in experiments?\n\nIntroduction\nThere have been many advances in machine learning methods which help machines understand human behavior better than ever. One of the most important aspects of human behavior is emotio... | [
" tweet dataset created by Wang et al. , CrowdFlower dataset"
] | 3,189 | qasper |
d00cf470-739d-4d1a-a211-ff8c60932613 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What evaluation metrics did look at?\n\nIntroduction\nBack to 42 BC, the philosopher Cicero has raised the issue that although there were many Oratory classes, there were none for Conversational skills BIBREF0 . He highlighted how imp... | [
"precision, recall, F1 and accuracy"
] | 17,790 | qasper |
d191bd11-756b-49fd-904f-8061f3dd0d74 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is MSD prediction?\n\nIntroduction\nThis 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 for... | [
"morphosyntactic descriptions (MSD)"
] | 3,045 | qasper |
d3ba995f-1f2e-45e1-b719-5305ec65e8e7 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is a normal reading paradigm?\n\nIntroduction\nHow humans process language has become increasingly relevant in natural language processing since physiological data during language understanding is more accessible and recorded wit... | [
"participants were instructed to read the sentences naturally, without any specific task other than comprehension"
] | 4,710 | qasper |
debd9630-227f-4838-a0c7-0592d12cb69e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What does an individual model consist of?\n\nIntroduction\nSemantic Role Labeling (SRL) has emerged as an important task in Natural Language Processing (NLP) due to its applicability in information extraction, question answering, and ... | [
"Bayesian model of garg2012unsupervised as our base monolingual model"
] | 5,270 | qasper |
e2ca6f36-f1ae-420f-b0b6-bb40f1f18e8e | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What crowdsourcing platform is used?\n\nIntroduction\nThere 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 ... | [
"They did not use any platform, instead they hired undergraduate students to do the annotation."
] | 8,637 | qasper |
f8600fa2-1930-4710-b768-ce6b870f83a4 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Is it valid to presume a bad medical wikipedia article should not contain much domain-specific jargon?\n\nIntroduction\nAs observed by a recent article of Nature News BIBREF0 , “Wikipedia is among the most frequently visited websites ... | [
"Unanswerable"
] | 6,204 | qasper |
f9892318-7907-42bc-82c4-60b7bd7de458 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What embeddings are used?\n\nIntroduction\nTo appear in Proceedings of International Workshop on Health Intelligence (W3PHIAI) of the 34th AAAI Conference on Artificial Intelligence, 2020.\nPhysician burnout is a growing concern, esti... | [
" simple lookup table embeddings learned from scratch, using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13"
] | 6,487 | qasper |
12b1519b-5494-430b-a496-aeace61cb3c6 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How was the dataset collected?\n\nIntroduction\nEnd-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automatic speech recog... | [
"crowdsourcing"
] | 3,596 | qasper |
1bdce58a-999d-43a1-9027-ac39ca42d0e3 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How they extract \"structured answer-relevant relation\"?\n\nIntroduction\nQuestion Generation (QG) is the task of automatically creating questions from a range of inputs, such as natural language text BIBREF0, knowledge base BIBREF1 ... | [
"Using the OpenIE toolbox and applying heuristic rules to select the most relevant relation."
] | 5,607 | qasper |
2280af3f-e80c-4403-958e-d7611f347ded | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What are the baselines used in the paper?\n\nIntroduction\nThe development of online peer-to-peer markets in the 1990s, galvanized by the launch of sites like eBay, fundamentally shifted the way buyers and sellers could connect [4]. T... | [
"GloVe vectors trained on Wikipedia Corpus with ensembling, and GloVe vectors trained on Airbnb Data without ensembling"
] | 3,509 | qasper |
2ced62c9-9941-46e1-b3c2-bc34b58175ca | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How many labels do the datasets have?\n\nIntroduction\nIn 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 d... | [
"719313"
] | 6,775 | qasper |
2ea40ad5-88fb-411f-9371-159acee22996 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which datasets did they use for evaluation?\n\nIntroduction\nQuestion 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 docum... | [
"Spoken-SQuAD"
] | 3,114 | qasper |
4d0299fe-867b-4840-9cb3-64b48a4d5fe4 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How do they match words before reordering them?\n\nIntroduction\nDeep Learning approaches have achieved impressive results on various NLP tasks BIBREF0 , BIBREF1 , BIBREF2 and have become the de facto approach for any NLP task. Howeve... | [
"CFILT-preorder system"
] | 2,977 | qasper |
51575a28-958f-43bb-816d-4bace044c84c | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"what debates dataset was used?\n\nIntroduction\nPublic 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 allo... | [
"Intelligence Squared Debates"
] | 3,172 | qasper |
5b001a07-b1fb-4930-84ae-8bc0c7a9136f | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What conclusions do the authors draw from their finding that the emotional appeal of ISIS and Catholic materials are similar?\n\nIntroduction\nSince its rise in 2013, the Islamic State of Iraq and Syria (ISIS) has utilized the Interne... | [
"both corpuses used words that aim to inspire readers while avoiding fear, actual words that lead to these effects are very different in the two contexts, our findings indicate that, using proper methods, automated analysis of large bodies of textual data can provide novel insight insight into extremist propaganda"... | 4,682 | qasper |
61955d8f-0835-4976-9fcf-fb8ab2e173ca | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is the approach of previous work?\n\nIntroduction\nAccurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines. If the language of a piece of text is known then... | [
"BIBREF11 that uses a character level n-gram language model, 'shallow' naive Bayes classifiers BIBREF12, BIBREF8, BIBREF13, BIBREF14, SVMs BIBREF15, BIBREF16 used an SVM with character n-gram, parts of speech tag features and some other engineered features, The winning approach for DSL 2015 used an ensemble naive B... | 2,610 | qasper |
6730eaa5-731c-458c-a5b2-6087fc4233d4 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What dataset does this approach achieve state of the art results on?\n\nData\nWe build and test our MMT models on the Multi30K dataset BIBREF21 . Each image in Multi30K contains one English (EN) description taken from Flickr30K BIBREF... | [
"the English-German dataset"
] | 2,509 | qasper |
6903da37-5be3-46f5-a28c-e392771ea734 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which shallow approaches did they experiment with?\n\nIntroduction\nThe distribution of textual content is typically very fast and catches user attention for only a short period of time BIBREF0 . For this reason, proper wording of the... | [
"SVM with linear kernel using bag-of-words features"
] | 3,079 | qasper |
69612c72-f5b9-4db0-b202-7dff97f1b2b5 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which model architectures do they test their word importance approach on?\n\nIntroduction\nNeural machine translation (NMT) has achieved the state-of-the-art results on a mass of language pairs with varying structural differences, suc... | [
"Transformer, RNN-Search model"
] | 5,822 | qasper |
6b46ace0-29f0-4e24-826a-296a4185bce3 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What are the baselines?\n\nIntroduction\nSpeech-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 o... | [
"Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder. It is trained from scratch on the ST-TED corpus.\n\nPre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-trai... | 7,491 | qasper |
6f8b229d-b0c7-402e-9623-e8820721eac3 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How do the backoff strategies work?\n\nIntroduction\nDespite the rapid progress of deep learning techniques on diverse supervised learning tasks, these models remain brittle to subtle shifts in the data distribution. Even when the per... | [
"Pass-through passes the possibly misspelled word as is, backoff to neutral word backs off to a word with similar distribution across classes and backoff to background model backs off to a more generic word recognition model trained with larger and less specialized corpus."
] | 5,902 | qasper |
8aba0f09-9221-4292-bb57-120408ec1ac4 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Who judged the irony accuracy, sentiment preservation and content preservation?\n\nIntroduction\nThe irony is a kind of figurative language, which is widely used on social media BIBREF0 . The irony is defined as a clash between the in... | [
"Irony accuracy is judged only by human ; senriment preservation and content preservation are judged both by human and using automatic metrics (ACC and BLEU)."
] | 6,084 | qasper |
950ba699-5d95-4298-b503-fe62053bf4d4 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How is a \"chunk of posts\" defined in this work?\n\nIntroduction\nSocial media platforms have made the spreading of fake news easier, faster as well as able to reach a wider audience. Social media offer another feature which is the a... | [
"chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account"
] | 2,888 | qasper |
9822e212-ff5d-459b-ae31-83ebf71946d7 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"what datasets did they use?\n\nIntroduction\nQuestion 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 e... | [
"3500 questions collected from the internet and books."
] | 2,874 | qasper |
a3ac65a8-392b-4e94-b97b-d02f9eacc613 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What is meant by closed test setting?\n\nIntroduction\nChinese word segmentation (CWS) is a task for Chinese natural language process to delimit word boundary. CWS is a basic and essential task for Chinese which is written without exp... | [
"closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation"
] | 5,082 | qasper |
aaf1990f-e706-43a9-abc7-9f6c84b64935 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Who were the experts used for annotation?\n\nIntroduction\nPrivacy policies are the documents which disclose the ways in which a company gathers, uses, shares and manages a user's data. As legal documents, they function using the prin... | [
"Individuals with legal training"
] | 5,279 | qasper |
ab600808-671c-4a75-892a-acb3a538963b | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"On what Text-Based Games are experiments performed?\n\nIntroduction\nText-based games became popular in the mid 80s with the game series Zork BIBREF1 resulting in many different text-based games being produced and published BIBREF2. T... | [
"CoinCollector , CookingWorld "
] | 6,937 | qasper |
b779540a-4cf6-4d58-8f0d-adbf57e88dab | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What argumentation phenomena encounter in actual data are now accounted for by this work?\n\nIntroduction\nThe art of argumentation has been studied since the early work of Aristotle, dating back to the 4th century BC BIBREF0 . It has... | [
"Unanswerable"
] | 20,062 | qasper |
d8f5db89-7605-4b20-9c02-3b43d2984c5f | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"How was the dataset annotated?\n\nIntroduction\nTask-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 ... | [
"intents are annotated manually with guidance from queries collected using a scoping crowdsourcing task"
] | 3,154 | qasper |
e570b6a3-8318-4d84-9cfb-647c68f5fa68 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"What metric is used for evaluation?\n\nIntroduction\nFollowing 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,... | [
"Precision, recall, F1, accuracy"
] | 4,221 | qasper |
eac84a75-a09b-4bd4-98ae-680b2f7322b6 | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Which hyperparameters were varied in the experiments on the four tasks?\n\nIntroduction\nMany research attempts have proposed novel features that improve the performance of learning algorithms in particular tasks. Such features are of... | [
"number of clusters, seed value in clustering, selection of word vectors, window size and dimension of embedding"
] | 3,565 | qasper |
ec861a9c-4856-4728-a41e-19d4231b903c | [{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"what are the sizes of both datasets?\n\nIntroduction\nText simplification aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning, which can help children, non-native speakers, and pe... | [
"WikiSmall 89 142 sentence pair and WikiLarge 298 761 sentence pairs. "
] | 3,186 | qasper |
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