id stringlengths 40 40 | pid stringlengths 42 42 | input stringlengths 8.37k 169k | output stringlengths 1 1.63k |
|---|---|---|---|
b3bd217287b8c765b0d461dc283afec779dbf039 | b3bd217287b8c765b0d461dc283afec779dbf039_0 | Q: Which query explanation method was preffered by the users in terms of correctness?
Text: Introduction
Natural language interfaces have been gaining significant popularity, enabling ordinary users to write and execute complex queries. One of the prominent paradigms for developing NL interfaces is semantic parsing, wh... | hybrid approach |
e8647f9dc0986048694c34ab9ce763b3167c3deb | e8647f9dc0986048694c34ab9ce763b3167c3deb_0 | Q: Do they conduct a user study where they show an NL interface with and without their explanation?
Text: Introduction
Natural language interfaces have been gaining significant popularity, enabling ordinary users to write and execute complex queries. One of the prominent paradigms for developing NL interfaces is semant... | No |
a0876fcbcb5a5944b412613e885703f14732676c | a0876fcbcb5a5944b412613e885703f14732676c_0 | Q: How do the users in the user studies evaluate reliability of a NL interface?
Text: Introduction
Natural language interfaces have been gaining significant popularity, enabling ordinary users to write and execute complex queries. One of the prominent paradigms for developing NL interfaces is semantic parsing, which is... | Unanswerable |
84d36bca06786070e49d3db784e42a51dd573d36 | 84d36bca06786070e49d3db784e42a51dd573d36_0 | Q: What was the task given to workers?
Text: Introduction
Crowdsourcing applications vary from basic, self-contained tasks such as image recognition or labeling BIBREF0 all the way to open-ended and creative endeavors such as collaborative writing, creative question proposal, or more general ideation BIBREF1 . Yet scal... | conceptualization task |
7af01e2580c332e2b5e8094908df4e43a29c8792 | 7af01e2580c332e2b5e8094908df4e43a29c8792_0 | Q: How was lexical diversity measured?
Text: Introduction
Crowdsourcing applications vary from basic, self-contained tasks such as image recognition or labeling BIBREF0 all the way to open-ended and creative endeavors such as collaborative writing, creative question proposal, or more general ideation BIBREF1 . Yet scal... | By computing number of unique responses and number of responses divided by the number of unique responses to that question for each of the questions |
c78f18606524539e4c573481e5bf1e0a242cc33c | c78f18606524539e4c573481e5bf1e0a242cc33c_0 | Q: How many responses did they obtain?
Text: Introduction
Crowdsourcing applications vary from basic, self-contained tasks such as image recognition or labeling BIBREF0 all the way to open-ended and creative endeavors such as collaborative writing, creative question proposal, or more general ideation BIBREF1 . Yet scal... | 1001 |
0cf6d52d7eafd43ff961377572bccefc29caf612 | 0cf6d52d7eafd43ff961377572bccefc29caf612_0 | Q: What crowdsourcing platform was used?
Text: Introduction
Crowdsourcing applications vary from basic, self-contained tasks such as image recognition or labeling BIBREF0 all the way to open-ended and creative endeavors such as collaborative writing, creative question proposal, or more general ideation BIBREF1 . Yet sc... | AMT |
ddd6ba43c4e1138156dd2ef03c25a4c4a47adad0 | ddd6ba43c4e1138156dd2ef03c25a4c4a47adad0_0 | Q: Are results reported only for English data?
Text: Introduction
Abstractive test summarization is an important text generation task. With the applying of the sequence-to-sequence model and the publication of large-scale datasets, the quality of the automatic generated summarization has been greatly improved BIBREF0 ,... | No |
bd99aba3309da96e96eab3e0f4c4c8c70b51980a | bd99aba3309da96e96eab3e0f4c4c8c70b51980a_0 | Q: Which existing models does this approach outperform?
Text: Introduction
Abstractive test summarization is an important text generation task. With the applying of the sequence-to-sequence model and the publication of large-scale datasets, the quality of the automatic generated summarization has been greatly improved ... | RNN-context, SRB, CopyNet, RNN-distract, DRGD |
73bb8b7d7e98ccb88bb19ecd2215d91dd212f50d | 73bb8b7d7e98ccb88bb19ecd2215d91dd212f50d_0 | Q: What human evaluation method is proposed?
Text: Introduction
Abstractive test summarization is an important text generation task. With the applying of the sequence-to-sequence model and the publication of large-scale datasets, the quality of the automatic generated summarization has been greatly improved BIBREF0 , B... | comparing the summary with the text instead of the reference and labeling the candidate bad if it is incorrect or irrelevant |
86e3136271a7b93991c8de5d310ab15a6ac5ab8c | 86e3136271a7b93991c8de5d310ab15a6ac5ab8c_0 | Q: How is human evaluation performed, what were the criteria?
Text: Introduction
Open-domain response generation BIBREF0, BIBREF1 for single-round short text conversation BIBREF2, aims at generating a meaningful and interesting response given a query from human users. Neural generation models are of growing interest in... | (1) Good (3 points): The response is grammatical, semantically relevant to the query, and more importantly informative and interesting, (2) Acceptable (2 points): The response is grammatical, semantically relevant to the query, but too trivial or generic, (3) Failed (1 point): The response has grammar mistakes or irrel... |
b48cd91219429f910b1ea6fcd6f4bd143ddf096f | b48cd91219429f910b1ea6fcd6f4bd143ddf096f_0 | Q: What automatic metrics are used?
Text: Introduction
Open-domain response generation BIBREF0, BIBREF1 for single-round short text conversation BIBREF2, aims at generating a meaningful and interesting response given a query from human users. Neural generation models are of growing interest in this topic due to their p... | BLEU, Distinct-1 & distinct-2 |
4f1a5eed730fdcf0e570f9118fc09ef2173c6a1b | 4f1a5eed730fdcf0e570f9118fc09ef2173c6a1b_0 | Q: What other kinds of generation models are used in experiments?
Text: Introduction
Open-domain response generation BIBREF0, BIBREF1 for single-round short text conversation BIBREF2, aims at generating a meaningful and interesting response given a query from human users. Neural generation models are of growing interes... | Seq2seq, CVAE, Hierarchical Gated Fusion Unit (HGFU), Mechanism-Aware Neural Machine (MANM) |
4bdad5a20750c878d1a891ef255621f6172b6a79 | 4bdad5a20750c878d1a891ef255621f6172b6a79_0 | Q: How does discrete latent variable has an explicit semantic meaning to improve the CVAE on short-text conversation?
Text: Introduction
Open-domain response generation BIBREF0, BIBREF1 for single-round short text conversation BIBREF2, aims at generating a meaningful and interesting response given a query from human us... | we connect each latent variable with a word in the vocabulary, thus each latent variable has an exact semantic meaning. |
2e3265d83d2a595293ed458152d3ee76ad19e244 | 2e3265d83d2a595293ed458152d3ee76ad19e244_0 | Q: What news dataset was used?
Text: Introduction
The web has provided researchers with vast amounts of unlabeled text data, and enabled the development of increasingly sophisticated language models which can achieve state of the art performance despite having no task specific training BIBREF0, BIBREF1, BIBREF2. It is ... | collection of headlines published by HuffPost BIBREF12 between 2012 and 2018 |
c2432884287dca4af355698a543bc0db67a8c091 | c2432884287dca4af355698a543bc0db67a8c091_0 | Q: How do they determine similarity between predicted word and topics?
Text: Introduction
The web has provided researchers with vast amounts of unlabeled text data, and enabled the development of increasingly sophisticated language models which can achieve state of the art performance despite having no task specific tr... | number of relevant output words as a function of the headline’s category label |
226ae469a65611f041de3ae545be0e386dba7d19 | 226ae469a65611f041de3ae545be0e386dba7d19_0 | Q: What is the language model pre-trained on?
Text: Introduction
The web has provided researchers with vast amounts of unlabeled text data, and enabled the development of increasingly sophisticated language models which can achieve state of the art performance despite having no task specific training BIBREF0, BIBREF1, ... | Wikipedea Corpus and BooksCorpus |
8ad815b29cc32c1861b77de938c7269c9259a064 | 8ad815b29cc32c1861b77de938c7269c9259a064_0 | Q: What languages are represented in the dataset?
Text: Introduction
Language Identification (LID) is the Natural Language Processing (NLP) task of automatically recognizing the language that a document is written in. While this task was called "solved" by some authors over a decade ago, it has seen a resurgence in rec... | EN, JA, ES, AR, PT, KO, TH, FR, TR, RU, IT, DE, PL, NL, EL, SV, FA, VI, FI, CS, UK, HI, DA, HU, NO, RO, SR, LV, BG, UR, TA, MR, BN, IN, KN, ET, SL, GU, CY, ZH, CKB, IS, LT, ML, SI, IW, NE, KM, MY, TL, KA, BO |
3f9ef59ac06db3f99b8b6f082308610eb2d3626a | 3f9ef59ac06db3f99b8b6f082308610eb2d3626a_0 | Q: Which existing language ID systems are tested?
Text: Introduction
Language Identification (LID) is the Natural Language Processing (NLP) task of automatically recognizing the language that a document is written in. While this task was called "solved" by some authors over a decade ago, it has seen a resurgence in rec... | langid.py library, encoder-decoder EquiLID system, GRU neural network LanideNN system, CLD2, CLD3 |
203d322743353aac8a3369220e1d023a78c2cae3 | 203d322743353aac8a3369220e1d023a78c2cae3_0 | Q: How was the one year worth of data collected?
Text: Introduction
Language Identification (LID) is the Natural Language Processing (NLP) task of automatically recognizing the language that a document is written in. While this task was called "solved" by some authors over a decade ago, it has seen a resurgence in rece... | Unanswerable |
557d1874f736d9d487eb823fe8f6dab4b17c3c42 | 557d1874f736d9d487eb823fe8f6dab4b17c3c42_0 | Q: Which language family does Mboshi belong to?
Text: Introduction
All over the world, languages are disappearing at an unprecedented rate, fostering the need for specific tools aimed to aid field linguists to collect, transcribe, analyze, and annotate endangered language data (e.g. BIBREF0, BIBREF1). A remarkable effo... | Bantu |
f41c401a4c6e1be768f8e68f774af3661c890ffd | f41c401a4c6e1be768f8e68f774af3661c890ffd_0 | Q: Does the paper report any alignment-only baseline?
Text: Introduction
All over the world, languages are disappearing at an unprecedented rate, fostering the need for specific tools aimed to aid field linguists to collect, transcribe, analyze, and annotate endangered language data (e.g. BIBREF0, BIBREF1). A remarkabl... | Yes |
09cd7ae01fe97bba230c109d0234fee80a1f013b | 09cd7ae01fe97bba230c109d0234fee80a1f013b_0 | Q: What is the dataset used in the paper?
Text: Introduction
All over the world, languages are disappearing at an unprecedented rate, fostering the need for specific tools aimed to aid field linguists to collect, transcribe, analyze, and annotate endangered language data (e.g. BIBREF0, BIBREF1). A remarkable effort in ... | French-Mboshi 5K corpus |
be3e020ba84bc53dfb90b8acaf549004b66e31e2 | be3e020ba84bc53dfb90b8acaf549004b66e31e2_0 | Q: How is the word segmentation task evaluated?
Text: Introduction
All over the world, languages are disappearing at an unprecedented rate, fostering the need for specific tools aimed to aid field linguists to collect, transcribe, analyze, and annotate endangered language data (e.g. BIBREF0, BIBREF1). A remarkable effo... | precision, recall, and F-measure on boundaries (BP, BR, BF), and tokens (WP, WR, WF), exact-match (X) metric |
24014a040447013a8cf0c0f196274667320db79f | 24014a040447013a8cf0c0f196274667320db79f_0 | Q: What are performance compared to former models?
Text: Introduction
Dependency parsing predicts the existence and type of linguistic dependency relations between words (as shown in Figure FIGREF1), which is a critical step in accomplishing deep natural language processing. Dependency parsing has been well developed B... | model overall still gives 1.0% higher average UAS and LAS than the previous best parser, BIAF, our model reports more than 1.0% higher average UAS than STACKPTR and 0.3% higher than BIAF |
9aa52b898d029af615b95b18b79078e9bed3d766 | 9aa52b898d029af615b95b18b79078e9bed3d766_0 | Q: How faster is training and decoding compared to former models?
Text: Introduction
Dependency parsing predicts the existence and type of linguistic dependency relations between words (as shown in Figure FIGREF1), which is a critical step in accomplishing deep natural language processing. Dependency parsing has been w... | Proposed vs best baseline:
Decoding: 8541 vs 8532 tokens/sec
Training: 8h vs 8h |
c431c142f5b82374746a2b2f18b40c6874f7131d | c431c142f5b82374746a2b2f18b40c6874f7131d_0 | Q: What datasets was the method evaluated on?
Text: Introduction
Neural Machine Translation (NMT) has made considerable progress in recent years BIBREF0 , BIBREF1 , BIBREF2 . Traditional NMT has relied solely on parallel sentence pairs for training data, which can be an expensive and scarce resource. This motivates the... | WMT18 EnDe bitext, WMT16 EnRo bitext, WMT15 EnFr bitext, We perform our experiments on WMT18 EnDe bitext, WMT16 EnRo bitext, and WMT15 EnFr bitext respectively. We use WMT Newscrawl for monolingual data (2007-2017 for De, 2016 for Ro, 2007-2013 for En, and 2007-2014 for Fr). For bitext, we filter out empty sentences an... |
7835d8f578386834c02e2c9aba78a345059d56ca | 7835d8f578386834c02e2c9aba78a345059d56ca_0 | Q: Is the model evaluated against a baseline?
Text: Introduction
Automatic dubbing can be regarded as an extension of the speech-to-speech translation (STST) task BIBREF0, which is generally seen as the combination of three sub-tasks: (i) transcribing speech to text in a source language (ASR), (ii) translating text fro... | No |
32e78ca99ba8b8423d4b21c54cd5309cb92191fc | 32e78ca99ba8b8423d4b21c54cd5309cb92191fc_0 | Q: How many people are employed for the subjective evaluation?
Text: Introduction
Automatic dubbing can be regarded as an extension of the speech-to-speech translation (STST) task BIBREF0, which is generally seen as the combination of three sub-tasks: (i) transcribing speech to text in a source language (ASR), (ii) tra... | 14 volunteers |
ffc5ad48b69a71e92295a66a9a0ff39548ab3cf1 | ffc5ad48b69a71e92295a66a9a0ff39548ab3cf1_0 | Q: What other embedding models are tested?
Text: Introduction
The prominent model for representing semantics of words is the distributional vector space model BIBREF2 and the prevalent approach for constructing these models is the distributional one which assumes that semantics of a word can be predicted from its conte... | GloVe embeddings trained by BIBREF10 on Wikipedia and Gigaword 5 (vocab: 400K, dim: 300), w2v-gn, Word2vec BIBREF5 trained on the Google News dataset (vocab: 3M, dim: 300), DeepWalk , node2vec |
1024f22110c436aa7a62a1022819bfe62dc0d336 | 1024f22110c436aa7a62a1022819bfe62dc0d336_0 | Q: How is performance measured?
Text: Introduction
The prominent model for representing semantics of words is the distributional vector space model BIBREF2 and the prevalent approach for constructing these models is the distributional one which assumes that semantics of a word can be predicted from its context, hence p... | To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding tra... |
f062723bda695716aa7cb0f27675b7fc0d302d4d | f062723bda695716aa7cb0f27675b7fc0d302d4d_0 | Q: How are rare words defined?
Text: Introduction
The prominent model for representing semantics of words is the distributional vector space model BIBREF2 and the prevalent approach for constructing these models is the distributional one which assumes that semantics of a word can be predicted from its context, hence pl... | judged by 10 raters on a [0,10] scale |
50e3fd6778dadf8ec0ff589aa8b18c61bdcacd41 | 50e3fd6778dadf8ec0ff589aa8b18c61bdcacd41_0 | Q: What other datasets are used?
Text: Introduction
There is a growing interest in research revolving around automated fake news detection and fact checking as its need increases due to the dangerous speed fake news spreads on social media BIBREF0. With as much as 68% of adults in the United States regularly consuming ... | WikiText-TL-39 |
c5980fe1a0c53bce1502cc674c8a2ed8c311f936 | c5980fe1a0c53bce1502cc674c8a2ed8c311f936_0 | Q: What is the size of the dataset?
Text: Introduction
There is a growing interest in research revolving around automated fake news detection and fact checking as its need increases due to the dangerous speed fake news spreads on social media BIBREF0. With as much as 68% of adults in the United States regularly consumi... | 3,206 |
7d3c036ec514d9c09c612a214498fc99bf163752 | 7d3c036ec514d9c09c612a214498fc99bf163752_0 | Q: What is the source of the dataset?
Text: Introduction
There is a growing interest in research revolving around automated fake news detection and fact checking as its need increases due to the dangerous speed fake news spreads on social media BIBREF0. With as much as 68% of adults in the United States regularly consu... | Online sites tagged as fake news site by Verafiles and NUJP and news website in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera |
ef7b62a705f887326b7ebacbd62567ee1f2129b3 | ef7b62a705f887326b7ebacbd62567ee1f2129b3_0 | Q: What were the baselines?
Text: Introduction
There is a growing interest in research revolving around automated fake news detection and fact checking as its need increases due to the dangerous speed fake news spreads on social media BIBREF0. With as much as 68% of adults in the United States regularly consuming news ... | Siamese neural network consisting of an embedding layer, a LSTM layer and a feed-forward layer with ReLU activations |
23d0637f8ae72ae343556ab135eedc7f4cb58032 | 23d0637f8ae72ae343556ab135eedc7f4cb58032_0 | Q: How do they show that acquiring names of places helps self-localization?
Text: Introduction
Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts ... | unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation, Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recogn... |
21c104d14ba3db7fe2cd804a191f9e6258208235 | 21c104d14ba3db7fe2cd804a191f9e6258208235_0 | Q: How do they evaluate how their model acquired words?
Text: Introduction
Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts and vocabulary on th... | PAR score |
d557752c4706b65dcdb7718272180c59d77fb7a7 | d557752c4706b65dcdb7718272180c59d77fb7a7_0 | Q: Which method do they use for word segmentation?
Text: Introduction
Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts and vocabulary on the bas... | unsupervised word segmentation method latticelm |
1bdf7e9f3f804930b2933ebd9207a3e000b27742 | 1bdf7e9f3f804930b2933ebd9207a3e000b27742_0 | Q: Does their model start with any prior knowledge of words?
Text: Introduction
Autonomous robots, such as service robots, operating in the human living environment with humans have to be able to perform various tasks and language communication. To this end, robots are required to acquire novel concepts and vocabulary ... | No |
a74886d789a5d7ebcf7f151bdfb862c79b6b8a12 | a74886d789a5d7ebcf7f151bdfb862c79b6b8a12_0 | Q: What were the baselines?
Text: Introduction
Misinformation and disinformation are two of the most pertinent and difficult challenges of the information age, exacerbated by the popularity of social media. In an effort to counter this, a significant amount of manual labour has been invested in fact checking claims, of... | a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30 |
e9ccc74b1f1b172224cf9f01e66b1fa9e34d2593 | e9ccc74b1f1b172224cf9f01e66b1fa9e34d2593_0 | Q: What metadata is included?
Text: Introduction
Misinformation and disinformation are two of the most pertinent and difficult challenges of the information age, exacerbated by the popularity of social media. In an effort to counter this, a significant amount of manual labour has been invested in fact checking claims, ... | besides claim, label and claim url, it also includes a claim ID, reason, category, speaker, checker, tags, claim entities, article title, publish data and claim date |
2948015c2a5cd6a7f2ad99b4622f7e4278ceb0d4 | 2948015c2a5cd6a7f2ad99b4622f7e4278ceb0d4_0 | Q: How many expert journalists were there?
Text: Introduction
Misinformation and disinformation are two of the most pertinent and difficult challenges of the information age, exacerbated by the popularity of social media. In an effort to counter this, a significant amount of manual labour has been invested in fact chec... | Unanswerable |
c33d0bc5484c38de0119c8738ffa985d1bd64424 | c33d0bc5484c38de0119c8738ffa985d1bd64424_0 | Q: Do the images have multilingual annotations or monolingual ones?
Text: Introduction
Recent advances in learning distributed representations for words (i.e., word embeddings) have resulted in improvements across numerous natural language understanding tasks BIBREF0 , BIBREF1 . These methods use unlabeled text corpora... | monolingual |
93b1b94b301a46251695db8194a2536639a22a88 | 93b1b94b301a46251695db8194a2536639a22a88_0 | Q: Could you learn such embedding simply from the image annotations and without using visual information?
Text: Introduction
Recent advances in learning distributed representations for words (i.e., word embeddings) have resulted in improvements across numerous natural language understanding tasks BIBREF0 , BIBREF1 . Th... | Yes |
e8029ec69b0b273954b4249873a5070c2a0edb8a | e8029ec69b0b273954b4249873a5070c2a0edb8a_0 | Q: How much important is the visual grounding in the learning of the multilingual representations?
Text: Introduction
Recent advances in learning distributed representations for words (i.e., word embeddings) have resulted in improvements across numerous natural language understanding tasks BIBREF0 , BIBREF1 . These met... | performance is significantly degraded without pixel data |
f4e17b14318b9f67d60a8a2dad1f6b506a10ab36 | f4e17b14318b9f67d60a8a2dad1f6b506a10ab36_0 | Q: How is the generative model evaluated?
Text: Introduction
The ability to determine entailment or contradiction between natural language text is essential for improving the performance in a wide range of natural language processing tasks. Recognizing Textual Entailment (RTE) is a task primarily designed to determine ... | Comparing BLEU score of model with and without attention |
fac052c4ad6b19a64d7db32fd08df38ad2e22118 | fac052c4ad6b19a64d7db32fd08df38ad2e22118_0 | Q: How do they evaluate their method?
Text: Introduction
Social media plays an important role in health informatics and Twitter has been one of the most influential social media channel for mining population-level health insights BIBREF0 , BIBREF1 , BIBREF2 . These insights range from forecasting of influenza epidemics... | Calinski-Harabasz score, t-SNE, UMAP |
aa54e12ff71c25b7cff1e44783d07806e89f8e54 | aa54e12ff71c25b7cff1e44783d07806e89f8e54_0 | Q: What is an example of a health-related tweet?
Text: Introduction
Social media plays an important role in health informatics and Twitter has been one of the most influential social media channel for mining population-level health insights BIBREF0 , BIBREF1 , BIBREF2 . These insights range from forecasting of influenz... | The health benefits of alcohol consumption are more limited than previously thought, researchers say |
1405824a6845082eae0458c94c4affd7456ad0f7 | 1405824a6845082eae0458c94c4affd7456ad0f7_0 | Q: Was the introduced LSTM+CNN model trained on annotated data in a supervised fashion?
Text: Introduction
Since Satoshi Nakamoto published the article "Bitcoin: A Peer-to-Peer Electronic Cash System" in 2008 BIBREF0 , and after the official launch of Bitcoin in 2009, technologies such as blockchain and cryptocurrency ... | Yes |
5be94c7c54593144ba2ac79729d7545f27c79d37 | 5be94c7c54593144ba2ac79729d7545f27c79d37_0 | Q: What is the challenge for other language except English
Text: Introduction
Offensive language in user-generated content on online platforms and its implications has been gaining attention over the last couple of years. This interest is sparked by the fact that many of the online social media platforms have come unde... | not researched as much as English |
32e8eda2183bcafbd79b22f757f8f55895a0b7b2 | 32e8eda2183bcafbd79b22f757f8f55895a0b7b2_0 | Q: How many categories of offensive language were there?
Text: Introduction
Offensive language in user-generated content on online platforms and its implications has been gaining attention over the last couple of years. This interest is sparked by the fact that many of the online social media platforms have come under ... | 3 |
b69f0438c1af4b9ed89e531c056d9812d4994016 | b69f0438c1af4b9ed89e531c056d9812d4994016_0 | Q: How large was the dataset of Danish comments?
Text: Introduction
Offensive language in user-generated content on online platforms and its implications has been gaining attention over the last couple of years. This interest is sparked by the fact that many of the online social media platforms have come under scrutiny... | 3600 user-generated comments |
2e9c6e01909503020070ec4faa6c8bf2d6c0af42 | 2e9c6e01909503020070ec4faa6c8bf2d6c0af42_0 | Q: Who were the annotators?
Text: Introduction
Offensive language in user-generated content on online platforms and its implications has been gaining attention over the last couple of years. This interest is sparked by the fact that many of the online social media platforms have come under scrutiny on how this type of ... | the author and the supervisor |
fc65f19a30150a0e981fb69c1f5720f0136325b0 | fc65f19a30150a0e981fb69c1f5720f0136325b0_0 | Q: Is is known whether Sina Weibo posts are censored by humans or some automatic classifier?
Text: Introduction
In 2019, Freedom in the World, a yearly survey produced by Freedom House that measures the degree of civil liberties and political rights in every nation, recorded the 13th consecutive year of decline in glob... | No |
5067e5eb2cddbb34b71e8b74ab9210cd46bb09c5 | 5067e5eb2cddbb34b71e8b74ab9210cd46bb09c5_0 | Q: Which matching features do they employ?
Text: Introduction
Natural Language Inference (NLI) is a crucial subtopic in Natural Language Processing (NLP). Most studies treat NLI as a classification problem, aiming at recognizing the relation types of hypothesis-premise sentence pairs, usually including “Entailment”, “C... | Matching features from matching sentences from various perspectives. |
03502826f4919e251edba1525f84dd42f21b0253 | 03502826f4919e251edba1525f84dd42f21b0253_0 | Q: How much better in terms of JSD measure did their model perform?
Text: Introduction
Recurrent neural network (RNN) based techniques such as language models are the most popular approaches for text generation. These RNN-based text generators rely on maximum likelihood estimation (MLE) solutions such as teacher forcin... | Unanswerable |
9368471073c66fefebc04f1820209f563a840240 | 9368471073c66fefebc04f1820209f563a840240_0 | Q: What does the Jensen-Shannon distance measure?
Text: Introduction
Recurrent neural network (RNN) based techniques such as language models are the most popular approaches for text generation. These RNN-based text generators rely on maximum likelihood estimation (MLE) solutions such as teacher forcing BIBREF0 (i.e. th... | Unanswerable |
981443fce6167b3f6cadf44f9f108d68c1a3f4ab | 981443fce6167b3f6cadf44f9f108d68c1a3f4ab_0 | Q: Which countries and languages do the political speeches and manifestos come from?
Text: Introduction
Modern media generate a large amount of content at an ever increasing rate. Keeping an unbiased view on what media report on requires to understand the political bias of texts. In many cases it is obvious which polit... | german |
6d0f2cce46bc962c6527f7b4a77721799f2455c6 | 6d0f2cce46bc962c6527f7b4a77721799f2455c6_0 | Q: Do changes in policies of the political actors account for all of the mistakes the model made?
Text: Introduction
Modern media generate a large amount of content at an ever increasing rate. Keeping an unbiased view on what media report on requires to understand the political bias of texts. In many cases it is obviou... | Yes |
5816ebf15e31bdf70e1de8234132e146d64e31eb | 5816ebf15e31bdf70e1de8234132e146d64e31eb_0 | Q: What model are the text features used in to provide predictions?
Text: Introduction
Modern media generate a large amount of content at an ever increasing rate. Keeping an unbiased view on what media report on requires to understand the political bias of texts. In many cases it is obvious which political bias an auth... | multinomial logistic regression |
5a9f94ae296dda06c8aec0fb389ce2f68940ea88 | 5a9f94ae296dda06c8aec0fb389ce2f68940ea88_0 | Q: By how much does their method outperform the multi-head attention model?
Text: Introduction
Automatic speech recognition (ASR) is the task to convert a continuous speech signal into a sequence of discrete characters, and it is a key technology to realize the interaction between human and machine. ASR has a great pot... | Their average improvement in Character Error Rate over the best MHA model was 0.33 percent points. |
85912b87b16b45cde79039447a70bd1f6f1f8361 | 85912b87b16b45cde79039447a70bd1f6f1f8361_0 | Q: How large is the corpus they use?
Text: Introduction
Automatic speech recognition (ASR) is the task to convert a continuous speech signal into a sequence of discrete characters, and it is a key technology to realize the interaction between human and machine. ASR has a great potential for various applications such as... | 449050 |
948327d7aa9f85943aac59e3f8613765861f97ff | 948327d7aa9f85943aac59e3f8613765861f97ff_0 | Q: Does each attention head in the decoder calculate the same output?
Text: Introduction
Automatic speech recognition (ASR) is the task to convert a continuous speech signal into a sequence of discrete characters, and it is a key technology to realize the interaction between human and machine. ASR has a great potential... | No |
cdf7e60150a166d41baed9dad539e3b93b544624 | cdf7e60150a166d41baed9dad539e3b93b544624_0 | Q: Which distributional methods did they consider?
Text: Introduction
Hierarchical relationships play a central role in knowledge representation and reasoning. Hypernym detection, i.e., the modeling of word-level hierarchies, has long been an important task in natural language processing. Starting with BIBREF0 , patter... | WeedsPrec BIBREF8, invCL BIBREF11, SLQS model, cosine similarity |
c06b5623c35b6fa7938340fa340269dc81d061e1 | c06b5623c35b6fa7938340fa340269dc81d061e1_0 | Q: Which benchmark datasets are used?
Text: Introduction
Hierarchical relationships play a central role in knowledge representation and reasoning. Hypernym detection, i.e., the modeling of word-level hierarchies, has long been an important task in natural language processing. Starting with BIBREF0 , pattern-based metho... | noun-noun subset of bless, leds BIBREF13, bless, wbless, bibless, hyperlex BIBREF20 |
d325a3c21660dbc481b4e839ff1a2d37dcc7ca46 | d325a3c21660dbc481b4e839ff1a2d37dcc7ca46_0 | Q: What hypernymy tasks do they study?
Text: Introduction
Hierarchical relationships play a central role in knowledge representation and reasoning. Hypernym detection, i.e., the modeling of word-level hierarchies, has long been an important task in natural language processing. Starting with BIBREF0 , pattern-based meth... | Detection, Direction, Graded Entailment |
eae13c9693ace504eab1f96c91b16a0627cd1f75 | eae13c9693ace504eab1f96c91b16a0627cd1f75_0 | Q: Do they repot results only on English data?
Text: Introduction
Multi-task learning (MTL) refers to machine learning approaches in which information and representations are shared to solve multiple, related tasks. Relative to single-task learning approaches, MTL often shows improved performance on some or all sub-tas... | Yes |
bcec22a75c1f899e9fcea4996457cf177c50c4c5 | bcec22a75c1f899e9fcea4996457cf177c50c4c5_0 | Q: What were the variables in the ablation study?
Text: Introduction
Multi-task learning (MTL) refers to machine learning approaches in which information and representations are shared to solve multiple, related tasks. Relative to single-task learning approaches, MTL often shows improved performance on some or all sub-... | (i) zero NER-specific BiRNN layers, (ii) zero RE-specific BiRNN layers, or (iii) zero task-specific BiRNN layers of any kind |
58f50397a075f128b45c6b824edb7a955ee8cba1 | 58f50397a075f128b45c6b824edb7a955ee8cba1_0 | Q: How many shared layers are in the system?
Text: Introduction
Multi-task learning (MTL) refers to machine learning approaches in which information and representations are shared to solve multiple, related tasks. Relative to single-task learning approaches, MTL often shows improved performance on some or all sub-tasks... | 1 |
9adcc8c4a10fa0d58f235b740d8d495ee622d596 | 9adcc8c4a10fa0d58f235b740d8d495ee622d596_0 | Q: How many additional task-specific layers are introduced?
Text: Introduction
Multi-task learning (MTL) refers to machine learning approaches in which information and representations are shared to solve multiple, related tasks. Relative to single-task learning approaches, MTL often shows improved performance on some o... | 2 for the ADE dataset and 3 for the CoNLL04 dataset |
91c81807374f2459990e5f9f8103906401abc5c2 | 91c81807374f2459990e5f9f8103906401abc5c2_0 | Q: What is barycentric Newton diagram?
Text: Introduction
With growing diversity in personal food preference and regional cuisine style, personalized information systems that can transform a recipe into any selected regional cuisine style that a user might prefer would help food companies and professional chefs create ... | The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates. |
2cc42d14c8c927939a6b8d06f4fdee0913042416 | 2cc42d14c8c927939a6b8d06f4fdee0913042416_0 | Q: Do they propose any solution to debias the embeddings?
Text: Introduction
Recent work in the word embeddings literature has shown that embeddings encode gender and racial biases, BIBREF0, BIBREF1, BIBREF2. These biases can have harmful effects in downstream tasks including coreference resolution, BIBREF3 and machine... | No |
b546f14feaa639e43aa64c799dc61b8ef480fb3d | b546f14feaa639e43aa64c799dc61b8ef480fb3d_0 | Q: How are these biases found?
Text: Introduction
Recent work in the word embeddings literature has shown that embeddings encode gender and racial biases, BIBREF0, BIBREF1, BIBREF2. These biases can have harmful effects in downstream tasks including coreference resolution, BIBREF3 and machine translation, BIBREF4, lead... | Unanswerable |
8568c82078495ab421ecbae38ddd692c867eac09 | 8568c82078495ab421ecbae38ddd692c867eac09_0 | Q: How many layers of self-attention does the model have?
Text: Introduction
Task-oriented chatbots are a type of dialogue generation system which tries to help the users accomplish specific tasks, such as booking a restaurant table or buying movie tickets, in a continuous and uninterrupted conversational interface and... | 1, 4, 8, 16, 32, 64 |
2ea382c676e418edd5327998e076a8c445d007a5 | 2ea382c676e418edd5327998e076a8c445d007a5_0 | Q: Is human evaluation performed?
Text: Introduction
Task-oriented chatbots are a type of dialogue generation system which tries to help the users accomplish specific tasks, such as booking a restaurant table or buying movie tickets, in a continuous and uninterrupted conversational interface and usually in as few steps... | No |
bd7a95b961af7caebf0430a7c9f675816c9c527f | bd7a95b961af7caebf0430a7c9f675816c9c527f_0 | Q: What are the three datasets used?
Text: Introduction
Task-oriented chatbots are a type of dialogue generation system which tries to help the users accomplish specific tasks, such as booking a restaurant table or buying movie tickets, in a continuous and uninterrupted conversational interface and usually in as few st... | DSTC2, M2M-sim-M, M2M-sim-R |
f011d6d5287339a35d00cd9ce1dfeabb1f3c0563 | f011d6d5287339a35d00cd9ce1dfeabb1f3c0563_0 | Q: Did they experiment with the corpus?
Text: Introduction
When a group of people communicate in a common channel there are often multiple conversations occurring concurrently. Often there is no explicit structure identifying conversations or their structure, such as in Internet Relay Chat (IRC), Google Hangout, and co... | Yes |
2ba0c7576eb5b84463a59ff190d4793b67f40ccc | 2ba0c7576eb5b84463a59ff190d4793b67f40ccc_0 | Q: How were the feature representations evaluated?
Text: Introduction
Neural networks for language processing have advanced rapidly in recent years. A key breakthrough was the introduction of transformer architectures BIBREF0 . One recent system based on this idea, BERT BIBREF1 , has proven to be extremely flexible: a ... | attention probes, using visualizations of the activations created by different pieces of text |
c58e60b99a6590e6b9a34de96c7606b004a4f169 | c58e60b99a6590e6b9a34de96c7606b004a4f169_0 | Q: What linguistic features were probed for?
Text: Introduction
Neural networks for language processing have advanced rapidly in recent years. A key breakthrough was the introduction of transformer architectures BIBREF0 . One recent system based on this idea, BERT BIBREF1 , has proven to be extremely flexible: a single... | dependency relation between two words, word sense |
6a099dfe354a79936b59d651ba0887d9f586eaaf | 6a099dfe354a79936b59d651ba0887d9f586eaaf_0 | Q: Does the paper describe experiments with real humans?
Text: Overinformativeness in referring expressions
Reference to objects is one of the most basic and prevalent uses of language. In order to refer, speakers must choose from among a wealth of referring expressions they have at their disposal. How does a speaker c... | Yes |
f748cb05becc60e7d47d34f4c5f94189bc184d33 | f748cb05becc60e7d47d34f4c5f94189bc184d33_0 | Q: What are bottleneck features?
Text: Introduction
Social media has become a popular medium for individuals to express opinions and concerns on issues impacting their lives BIBREF0 , BIBREF1 , BIBREF2 . In countries without adequate internet infrastructure, like Uganda, communities often use phone-in talk shows on loc... | Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese, South African English, These features are typically obtained by training a deep neural network jointly on several languages for which labelled data is available., The final shared layer often has a lower dimensionality than the inp... |
1a06b7a2097ebbad0afc787ea0756db6af3dadf4 | 1a06b7a2097ebbad0afc787ea0756db6af3dadf4_0 | Q: What languages are considered?
Text: Introduction
Social media has become a popular medium for individuals to express opinions and concerns on issues impacting their lives BIBREF0 , BIBREF1 , BIBREF2 . In countries without adequate internet infrastructure, like Uganda, communities often use phone-in talk shows on lo... | Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese |
390aa2d733bd73699899a37e65c0dee4668d2cd8 | 390aa2d733bd73699899a37e65c0dee4668d2cd8_0 | Q: Do they compare speed performance of their model compared to the ones using the LID model?
Text: Introduction
Code-switching (CS) speech is defined as the alternation of languages in an utterance, it is a pervasive communicative phenomenon in multilingual communities. Therefore, developing a CS speech recognition (C... | Unanswerable |
86083a02cc9a80b31cac912c42c710de2ef4adfd | 86083a02cc9a80b31cac912c42c710de2ef4adfd_0 | Q: How do they obtain language identities?
Text: Introduction
Code-switching (CS) speech is defined as the alternation of languages in an utterance, it is a pervasive communicative phenomenon in multilingual communities. Therefore, developing a CS speech recognition (CSSR) system is of great interest.
However, the CS s... | model is trained to predict language IDs as well as the subwords, we add language IDs in the CS point of transcriptio |
29e5e055e01fdbf7b90d5907158676dd3169732d | 29e5e055e01fdbf7b90d5907158676dd3169732d_0 | Q: What other multimodal knowledge base embedding methods are there?
Text: Introduction
Knowledge bases (KB) are an essential part of many computational systems with applications in search, structured data management, recommendations, question answering, and information retrieval. However, KBs often suffer from incompl... | merging, concatenating, or averaging the entity and its features to compute its embeddings, graph embedding approaches, matrix factorization to jointly embed KB and textual relations |
6c4d121d40ce6318ecdc141395cdd2982ba46cff | 6c4d121d40ce6318ecdc141395cdd2982ba46cff_0 | Q: What is the data selection paper in machine translation
Text: Introduction
Machine Reading Comprehension (MRC) has gained growing interest in the research community BIBREF0 , BIBREF1 . In an MRC task, the machine reads a text passage and a question, and generates (or selects) an answer based on the passage. This req... | BIBREF7, BIBREF26 |
b1457feb6cdbf4fb19c8e87e1cd43981bc991c4c | b1457feb6cdbf4fb19c8e87e1cd43981bc991c4c_0 | Q: Do they compare computational time of AM-softmax versus Softmax?
Text: Introduction
Speaker Recognition is an essential task with applications in biometric authentication, identification, and security among others BIBREF0 . The field is divided into two main subtasks: Speaker Identification and Speaker Verification.... | No |
46bca122a87269b20e252838407a2f88f644ded8 | 46bca122a87269b20e252838407a2f88f644ded8_0 | Q: Do they visualize the difference between AM-Softmax and regular softmax?
Text: Introduction
Speaker Recognition is an essential task with applications in biometric authentication, identification, and security among others BIBREF0 . The field is divided into two main subtasks: Speaker Identification and Speaker Verif... | Yes |
7c792cda220916df40edb3107e405c86455822ed | 7c792cda220916df40edb3107e405c86455822ed_0 | Q: what metrics were used for evaluation?
Text: Introduction
With the development of digital media technology and popularity of Mobile Internet, online visual content has increased rapidly in recent couple of years. Subsequently, visual content analysis for retrieving BIBREF0 , BIBREF1 and understanding becomes a funda... | METEOR |
b3fcab006a9e51a0178a1f64d1d084a895bd8d5c | b3fcab006a9e51a0178a1f64d1d084a895bd8d5c_0 | Q: what are the state of the art methods?
Text: Introduction
With the development of digital media technology and popularity of Mobile Internet, online visual content has increased rapidly in recent couple of years. Subsequently, visual content analysis for retrieving BIBREF0 , BIBREF1 and understanding becomes a funda... | S2VT, RGB (VGG), RGB (VGG)+Flow (AlexNet), LSTM-E (VGG), LSTM-E (C3D) and Yao et al. |
864b5c1fe8c744f80a55e87421b29d6485b7efd0 | 864b5c1fe8c744f80a55e87421b29d6485b7efd0_0 | Q: What evaluation metrics do they use?
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Introduction
Electronic Health Records (EHR) have become ubiquitous in recent years in the United States, owing much to the The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. BI... | Precision, Recall and INLINEFORM0 score |
d469c7de5c9e6dd8a901190e95688c446f12118f | d469c7de5c9e6dd8a901190e95688c446f12118f_0 | Q: What performance is achieved?
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Electronic Health Records (EHR) have become ubiquitous in recent years in the United States, owing much to the The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. BIBREF0 T... | Unanswerable |
0a050658d09f3c6e21e9ab828dc18e59b147cf7c | 0a050658d09f3c6e21e9ab828dc18e59b147cf7c_0 | Q: Do they use BERT?
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Introduction
Electronic Health Records (EHR) have become ubiquitous in recent years in the United States, owing much to the The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. BIBREF0 Their ubiquit... | No |
fd80a7162fde83077ed82ae41d521d774f74340a | fd80a7162fde83077ed82ae41d521d774f74340a_0 | Q: What is their baseline?
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Introduction
Electronic Health Records (EHR) have become ubiquitous in recent years in the United States, owing much to the The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. BIBREF0 Their u... | Burckhardt et al. BIBREF22, Liu et al. BIBREF18, Dernoncourt et al. BIBREF9, Yang et al. BIBREF10 |
4d4739682d540878a94d8227412e9e1ec1bb3d39 | 4d4739682d540878a94d8227412e9e1ec1bb3d39_0 | Q: Which two datasets is the system tested on?
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Introduction
Electronic Health Records (EHR) have become ubiquitous in recent years in the United States, owing much to the The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2... | 2014 i2b2 de-identification challenge data set BIBREF2, nursing notes corpus BIBREF3 |
6baf5d7739758bdd79326ce8f50731c785029802 | 6baf5d7739758bdd79326ce8f50731c785029802_0 | Q: Which four languages do they experiment with?
Text: Introduction
Speech conveys human emotions most naturally. In recent years there has been an increased research interest in speech emotion recognition domain. The first step in a typical SER system is extracting linguistic and acoustic features from speech signal. ... | German, English, Italian, Chinese |
5c4c8e91d28935e1655a582568cc9d94149da2b2 | 5c4c8e91d28935e1655a582568cc9d94149da2b2_0 | Q: Does DCA or GMM-based attention perform better in experiments?
Text: Introduction
Sequence-to-sequence models that use an attention mechanism to align the input and output sequences BIBREF0, BIBREF1 are currently the predominant paradigm in end-to-end TTS. Approaches based on the seminal Tacotron system BIBREF2 have... | About the same performance |
e4024db40f4b8c1ce593f53b28718e52d5007cd2 | e4024db40f4b8c1ce593f53b28718e52d5007cd2_0 | Q: How they compare varioius mechanisms in terms of naturalness?
Text: Introduction
Sequence-to-sequence models that use an attention mechanism to align the input and output sequences BIBREF0, BIBREF1 are currently the predominant paradigm in end-to-end TTS. Approaches based on the seminal Tacotron system BIBREF2 have ... | using mean opinion score (MOS) naturalness judgments produced by a crowd-sourced pool of raters |
3f326c003be29c8eac76b24d6bba9608c75aa7ea | 3f326c003be29c8eac76b24d6bba9608c75aa7ea_0 | Q: What evaluation metric is used?
Text: Introduction
The detection of offensive language has become an important topic as the online community has grown, as so too have the number of bad actors BIBREF2. Such behavior includes, but is not limited to, trolling in public discussion forums BIBREF3 and via social media BIB... | F1 and Weighted-F1 |
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