id stringlengths 40 40 | pid stringlengths 42 42 | input stringlengths 8.37k 169k | output stringlengths 1 1.63k |
|---|---|---|---|
61272b1d0338ed7708cf9ed9c63060a6a53e97a2 | 61272b1d0338ed7708cf9ed9c63060a6a53e97a2_0 | Q: What was their performance on the dataset?
Text: Introduction
In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especially how people express their opinions and comments. The extraction of useful information (such as people's opinion abo... | accuracy of 82.6% |
53b02095ba7625d85721692fce578654f66bbdf0 | 53b02095ba7625d85721692fce578654f66bbdf0_0 | Q: How large is the dataset?
Text: Introduction
In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especially how people express their opinions and comments. The extraction of useful information (such as people's opinion about companies bran... | Unanswerable |
0cd0755ac458c3bafbc70e4268c1e37b87b9721b | 0cd0755ac458c3bafbc70e4268c1e37b87b9721b_0 | Q: Did the authors use crowdsourcing platforms?
Text: 0pt0.03.03 *
0pt0.030.03 *
0pt0.030.03
We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common... | Yes |
0cd0755ac458c3bafbc70e4268c1e37b87b9721b | 0cd0755ac458c3bafbc70e4268c1e37b87b9721b_1 | Q: Did the authors use crowdsourcing platforms?
Text: 0pt0.03.03 *
0pt0.030.03 *
0pt0.030.03
We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common... | Yes |
c1ce652085ef9a7f02cb5c363ce2b8757adbe213 | c1ce652085ef9a7f02cb5c363ce2b8757adbe213_0 | Q: How was the dataset collected?
Text: 0pt0.03.03 *
0pt0.030.03 *
0pt0.030.03
We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having ... | crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk) |
96be67b1729c3a91ddf0ec7d6a80f2aa75e30a30 | 96be67b1729c3a91ddf0ec7d6a80f2aa75e30a30_0 | Q: What language do the agents talk in?
Text: 0pt0.03.03 *
0pt0.030.03 *
0pt0.030.03
We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: h... | English |
b85ab5f862221fac819cf2fef239bcb08b9cafc6 | b85ab5f862221fac819cf2fef239bcb08b9cafc6_0 | Q: What evaluation metrics did the authors look at?
Text: 0pt0.03.03 *
0pt0.030.03 *
0pt0.030.03
We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a co... | localization accuracy |
7e34501255b89d64b9598b409d73f96489aafe45 | 7e34501255b89d64b9598b409d73f96489aafe45_0 | Q: What data did they use?
Text: 0pt0.03.03 *
0pt0.030.03 *
0pt0.030.03
We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tou... | dataset on Mechanical Turk involving human perception, action and communication |
e854edcc5e9111922e6e120ae17d062427c27ec1 | e854edcc5e9111922e6e120ae17d062427c27ec1_0 | Q: Do the authors report results only on English data?
Text: Introduction
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIBREF2... | Unanswerable |
e854edcc5e9111922e6e120ae17d062427c27ec1 | e854edcc5e9111922e6e120ae17d062427c27ec1_1 | Q: Do the authors report results only on English data?
Text: Introduction
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIBREF2... | Unanswerable |
bd6cec2ab620e67b3e0e7946fc045230e6906020 | bd6cec2ab620e67b3e0e7946fc045230e6906020_0 | Q: How is the accuracy of the system measured?
Text: Introduction
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIBREF2 . Due t... | F1 score of 0.71 for this task without any specific training, simply by choosing a threshold below which all sentence pairs are considered duplicates, distances between duplicate and non-duplicate questions using different embedding systems |
4b0ba460ae3ba7a813f204abd16cf631b871baca | 4b0ba460ae3ba7a813f204abd16cf631b871baca_0 | Q: How is an incoming claim used to retrieve similar factchecked claims?
Text: Introduction
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true i... | text clustering on the embeddings of texts |
63b0c93f0452d0e1e6355de1d0f3ff0fd67939fb | 63b0c93f0452d0e1e6355de1d0f3ff0fd67939fb_0 | Q: What existing corpus is used for comparison in these experiments?
Text: Introduction
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true infor... | Quora duplicate question dataset BIBREF22 |
d27f23bcd80b12f6df8e03e65f9b150444925ecf | d27f23bcd80b12f6df8e03e65f9b150444925ecf_0 | Q: What are the components in the factchecking algorithm?
Text: Introduction
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIB... | Unanswerable |
b11ee27f3de7dd4a76a1f158dc13c2331af37d9f | b11ee27f3de7dd4a76a1f158dc13c2331af37d9f_0 | Q: What is the baseline?
Text: Introduction
Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, which allow systems to... | path ranking-based KGC (PRKGC) |
7aba5e4483293f5847caad144ee0791c77164917 | 7aba5e4483293f5847caad144ee0791c77164917_0 | Q: What dataset was used in the experiment?
Text: Introduction
Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, whi... | WikiHop |
565d668947ffa6d52dad019af79289420505889b | 565d668947ffa6d52dad019af79289420505889b_0 | Q: Did they use any crowdsourcing platform?
Text: Introduction
Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, whi... | Yes |
565d668947ffa6d52dad019af79289420505889b | 565d668947ffa6d52dad019af79289420505889b_1 | Q: Did they use any crowdsourcing platform?
Text: Introduction
Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, whi... | Yes |
d83304c70fe66ae72e78aa1d183e9f18b7484cd6 | d83304c70fe66ae72e78aa1d183e9f18b7484cd6_0 | Q: How was the dataset annotated?
Text: Introduction
Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, which allow s... | True, Likely (i.e. Answerable), or Unsure (i.e. Unanswerable), why they are unsure from two choices (“Not stated in the article” or “Other”), The “summary” text boxes |
e90ac9ee085dc2a9b6fe132245302bbce5f3f5ab | e90ac9ee085dc2a9b6fe132245302bbce5f3f5ab_0 | Q: What is the source of the proposed dataset?
Text: Introduction
Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, ... | Unanswerable |
5b029ad0d20b516ec11967baaf7d2006e8d7199f | 5b029ad0d20b516ec11967baaf7d2006e8d7199f_0 | Q: How many label options are there in the multi-label task?
Text: Introduction
Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real... | two labels |
79bd2ad4cb5c630ce69d5a859ed118132cae62d7 | 79bd2ad4cb5c630ce69d5a859ed118132cae62d7_0 | Q: What is the interannotator agreement of the crowd sourced users?
Text: Introduction
Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people po... | Unanswerable |
d3a1a53521f252f869fdae944db986931d9ffe48 | d3a1a53521f252f869fdae944db986931d9ffe48_0 | Q: Who are the experts?
Text: Introduction
Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions a... | political pundits of the Washington Post |
d3a1a53521f252f869fdae944db986931d9ffe48 | d3a1a53521f252f869fdae944db986931d9ffe48_1 | Q: Who are the experts?
Text: Introduction
Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions a... | the experts in the field |
38e11663b03ac585863742044fd15a0e875ae9ab | 38e11663b03ac585863742044fd15a0e875ae9ab_0 | Q: Who is the crowd in these experiments?
Text: Introduction
Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages abou... | peoples' sentiments expressed over social media |
14421b7ae4459b647033b3ccba635d4ba7bb114b | 14421b7ae4459b647033b3ccba635d4ba7bb114b_0 | Q: How do you establish the ground truth of who won a debate?
Text: Introduction
Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post rea... | experts in Washington Post |
52f7e42fe8f27d800d1189251dfec7446f0e1d3b | 52f7e42fe8f27d800d1189251dfec7446f0e1d3b_0 | Q: How much better is performance of proposed method than state-of-the-art methods in experiments?
Text: Introduction
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the m... | Accuracy of best proposed method KANE (LSTM+Concatenation) are 0.8011, 0.8592, 0.8605 compared to best state-of-the art method R-GCN + LR 0.7721, 0.8193, 0.8229 on three datasets respectively. |
00e6324ecd454f5d4b2a4b27fcf4104855ff8ee2 | 00e6324ecd454f5d4b2a4b27fcf4104855ff8ee2_0 | Q: What further analysis is done?
Text: Introduction
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs are usually encoded in t... | we use t-SNE tool BIBREF27 to visualize the learned embedding |
aa0d67c2a1bc222d1f2d9e5d51824352da5bb6dc | aa0d67c2a1bc222d1f2d9e5d51824352da5bb6dc_0 | Q: What seven state-of-the-art methods are used for comparison?
Text: Introduction
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts i... | TransE, TransR and TransH, PTransE, and ALL-PATHS, R-GCN BIBREF24 and KR-EAR BIBREF26 |
cf0085c1d7bd9bc9932424e4aba4e6812d27f727 | cf0085c1d7bd9bc9932424e4aba4e6812d27f727_0 | Q: What three datasets are used to measure performance?
Text: Introduction
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs ar... | FB24K, DBP24K, Game30K |
cf0085c1d7bd9bc9932424e4aba4e6812d27f727 | cf0085c1d7bd9bc9932424e4aba4e6812d27f727_1 | Q: What three datasets are used to measure performance?
Text: Introduction
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs ar... | Freebase BIBREF0, DBpedia BIBREF1 and a self-construction game knowledge graph |
586b7470be91efe246c3507b05e30651ea6b9832 | 586b7470be91efe246c3507b05e30651ea6b9832_0 | Q: How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner?
Text: Introduction
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowled... | To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. |
31b20a4bab09450267dfa42884227103743e3426 | 31b20a4bab09450267dfa42884227103743e3426_0 | Q: What are recent works on knowedge graph embeddings authors mention?
Text: Introduction
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The ... | entity types or concepts BIBREF13, relations paths BIBREF17, textual descriptions BIBREF11, BIBREF12, logical rules BIBREF23, deep neural network models BIBREF24 |
45306b26447ea4b120655d6bb2e3636079d3d6e0 | 45306b26447ea4b120655d6bb2e3636079d3d6e0_0 | Q: Do they report results only on English data?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred t... | Yes |
0c08af6e4feaf801185f2ec97c4da04c8b767ad6 | 0c08af6e4feaf801185f2ec97c4da04c8b767ad6_0 | Q: Do the authors mention any confounds to their study?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been re... | No |
6412e97373e8e9ae3aa20aa17abef8326dc05450 | 6412e97373e8e9ae3aa20aa17abef8326dc05450_0 | Q: What baseline model is used?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-text... | Human evaluators |
957bda6b421ef7d2839c3cec083404ac77721f14 | 957bda6b421ef7d2839c3cec083404ac77721f14_0 | Q: What stylistic features are used to detect drunk texts?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been... | LDA unigrams (Presence/Count), POS Ratio, #Named Entity Mentions, #Discourse Connectors, Spelling errors, Repeated characters, Capitalisation, Length, Emoticon (Presence/Count )
and Sentiment Ratio |
957bda6b421ef7d2839c3cec083404ac77721f14 | 957bda6b421ef7d2839c3cec083404ac77721f14_1 | Q: What stylistic features are used to detect drunk texts?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been... | LDA unigrams (Presence/Count), POS Ratio, #Named Entity Mentions, #Discourse Connectors, Spelling errors, Repeated characters, Capitalization, Length, Emoticon (Presence/Count), Sentiment Ratio. |
368317b4fd049511e00b441c2e9550ded6607c37 | 368317b4fd049511e00b441c2e9550ded6607c37_0 | Q: Is the data acquired under distant supervision verified by humans at any stage?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popula... | Yes |
b3ec918827cd22b16212265fcdd5b3eadee654ae | b3ec918827cd22b16212265fcdd5b3eadee654ae_0 | Q: What hashtags are used for distant supervision?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referre... | Unanswerable |
387970ebc7ef99f302f318d047f708274c0e8f21 | 387970ebc7ef99f302f318d047f708274c0e8f21_0 | Q: Do the authors equate drunk tweeting with drunk texting?
Text: Introduction
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has be... | Yes |
2fffff59e57b8dbcaefb437a6b3434fc137f813b | 2fffff59e57b8dbcaefb437a6b3434fc137f813b_0 | Q: What corpus was the source of the OpenIE extractions?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specif... | domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining |
2fffff59e57b8dbcaefb437a6b3434fc137f813b | 2fffff59e57b8dbcaefb437a6b3434fc137f813b_1 | Q: What corpus was the source of the OpenIE extractions?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specif... | Unanswerable |
eb95af36347ed0e0808e19963fe4d058e2ce3c9f | eb95af36347ed0e0808e19963fe4d058e2ce3c9f_0 | Q: What is the accuracy of the proposed technique?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Au... | 51.7 and 51.6 on 4th and 8th grade question sets with no curated knowledge. 47.5 and 48.0 on 4th and 8th grade question sets when both solvers are given the same knowledge |
cd1792929b9fa5dd5b1df0ae06fc6aece4c97424 | cd1792929b9fa5dd5b1df0ae06fc6aece4c97424_0 | Q: Is an entity linking process used?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automatically c... | No |
65d34041ffa4564385361979a08706b10b92ebc7 | 65d34041ffa4564385361979a08706b10b92ebc7_0 | Q: Are the OpenIE extractions all triples?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automatica... | No |
e215fa142102f7f9eeda9c9eb8d2aeff7f2a33ed | e215fa142102f7f9eeda9c9eb8d2aeff7f2a33ed_0 | Q: What method was used to generate the OpenIE extractions?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-spe... | for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S, take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ |
a8545f145d5ea2202cb321c8f93e75ad26fcf4aa | a8545f145d5ea2202cb321c8f93e75ad26fcf4aa_0 | Q: Can the method answer multi-hop questions?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automat... | Yes |
417dabd43d6266044d38ed88dbcb5fdd7a426b22 | 417dabd43d6266044d38ed88dbcb5fdd7a426b22_0 | Q: What was the textual source to which OpenIE was applied?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-spe... | domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining |
fed230cef7c130f6040fb04304a33bbc17ca3a36 | fed230cef7c130f6040fb04304a33bbc17ca3a36_0 | Q: What OpenIE method was used to generate the extractions?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-spe... | for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S, take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ |
7917d44e952b58ea066dc0b485d605c9a1fe3dda | 7917d44e952b58ea066dc0b485d605c9a1fe3dda_0 | Q: Is their method capable of multi-hop reasoning?
Text: Introduction
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Au... | Yes |
7d5ba230522df1890619dedcfb310160958223c1 | 7d5ba230522df1890619dedcfb310160958223c1_0 | Q: Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?
Text: Introduction
Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of ... | Yes |
a48cc6d3d322a7b159ff40ec162a541bf74321eb | a48cc6d3d322a7b159ff40ec162a541bf74321eb_0 | Q: What evaluation is conducted?
Text: Introduction
Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of WSD for the Russian language BIBREF0 , BIBREF1 , BIBREF2 . T... | Word Sense Induction & Disambiguation |
2bc0bb7d3688fdd2267c582ca593e2ce72718a91 | 2bc0bb7d3688fdd2267c582ca593e2ce72718a91_0 | Q: Which corpus of synsets are used?
Text: Introduction
Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of WSD for the Russian language BIBREF0 , BIBREF1 , BIBREF2... | Wiktionary |
8c073b7ea8cb5cc54d7fecb8f4bf88c1fb621b19 | 8c073b7ea8cb5cc54d7fecb8f4bf88c1fb621b19_0 | Q: What measure of semantic similarity is used?
Text: Introduction
Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of WSD for the Russian language BIBREF0 , BIBREF... | cosine similarity |
dcb18516369c3cf9838e83168357aed6643ae1b8 | dcb18516369c3cf9838e83168357aed6643ae1b8_0 | Q: Which retrieval system was used for baselines?
Text: Introduction
Factoid Question Answering (QA) aims to extract answers, from an underlying knowledge source, to information seeking questions posed in natural language. Depending on the knowledge source available there are two main approaches for factoid QA. Structu... | The dataset comes with a ranked set of relevant documents. Hence the baselines do not use a retrieval system. |
f46a907360d75ad566620e7f6bf7746497b6e4a9 | f46a907360d75ad566620e7f6bf7746497b6e4a9_0 | Q: What word embeddings were used?
Text: Introduction
Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of persons, organizations, locati... | Kyubyong Park, Edouard Grave et al BIBREF11 |
79d999bdf8a343ce5b2739db3833661a1deab742 | 79d999bdf8a343ce5b2739db3833661a1deab742_0 | Q: What type of errors were produced by the BLSTM-CNN-CRF system?
Text: Introduction
Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of... | No extraction, No annotation, Wrong range, Wrong tag, Wrong range and tag |
71d59c36225b5ee80af11d3568bdad7425f17b0c | 71d59c36225b5ee80af11d3568bdad7425f17b0c_0 | Q: How much better was the BLSTM-CNN-CRF than the BLSTM-CRF?
Text: Introduction
Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of pers... | Best BLSTM-CNN-CRF had F1 score 86.87 vs 86.69 of best BLSTM-CRF |
efc65e5032588da4a134d121fe50d49fe8fe5e8c | efc65e5032588da4a134d121fe50d49fe8fe5e8c_0 | Q: What supplemental tasks are used for multitask learning?
Text: Introduction
Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumul... | Multitask learning is used for the task of predicting relevance of a comment on a different question to a given question, where the supplemental tasks are predicting relevance between the questions, and between the comment and the corresponding question |
a30958c7123d1ad4723dcfd19d8346ccedb136d5 | a30958c7123d1ad4723dcfd19d8346ccedb136d5_0 | Q: Is the improvement actually coming from using an RNN?
Text: Introduction
Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulate... | No |
08333e4dd1da7d6b5e9b645d40ec9d502823f5d7 | 08333e4dd1da7d6b5e9b645d40ec9d502823f5d7_0 | Q: How much performance gap between their approach and the strong handcrafted method?
Text: Introduction
Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number ... | 0.007 MAP on Task A, 0.032 MAP on Task B, 0.055 MAP on Task C |
bc1bc92920a757d5ec38007a27d0f49cb2dde0d1 | bc1bc92920a757d5ec38007a27d0f49cb2dde0d1_0 | Q: What is a strong feature-based method?
Text: Introduction
Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large colle... | Unanswerable |
942eb1f7b243cdcfd47f176bcc71de2ef48a17c4 | 942eb1f7b243cdcfd47f176bcc71de2ef48a17c4_0 | Q: Did they experimnet in other languages?
Text: Introduction
Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large coll... | Yes |
9bffc9a9c527e938b2a95ba60c483a916dbd1f6b | 9bffc9a9c527e938b2a95ba60c483a916dbd1f6b_0 | Q: Do they use multi-attention heads?
Text: Introduction
Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over “opinion targets” that explicitly appear in the sentence. For example, given ... | Yes |
8434974090491a3c00eed4f22a878f0b70970713 | 8434974090491a3c00eed4f22a878f0b70970713_0 | Q: How big is their model?
Text: Introduction
Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over “opinion targets” that explicitly appear in the sentence. For example, given a sentence ... | Proposed model has 1.16 million parameters and 11.04 MB. |
b67420da975689e47d3ea1c12b601851018c4071 | b67420da975689e47d3ea1c12b601851018c4071_0 | Q: How is their model different from BERT?
Text: Introduction
Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over “opinion targets” that explicitly appear in the sentence. For example, g... | overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer. |
a4e66e842be1438e5cd8d7cb2a2c589f494aee27 | a4e66e842be1438e5cd8d7cb2a2c589f494aee27_0 | Q: Which tested technique was the worst performer?
Text: Introduction
Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.
Many recent advances in natural language processing on emotions have focused on product reviews BIB... | Depeche + SVM |
cb78e280e3340b786e81636431834b75824568c3 | cb78e280e3340b786e81636431834b75824568c3_0 | Q: How many emotions do they look at?
Text: Introduction
Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.
Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and twee... | 9 |
2941874356e98eb2832ba22eae9cb08ec8ce0308 | 2941874356e98eb2832ba22eae9cb08ec8ce0308_0 | Q: What are the baseline benchmarks?
Text: Introduction
Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.
Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweet... | TF-IDF + SVM, Depeche + SVM, NRC + SVM, TF-NRC + SVM, Doc2Vec + SVM, Hierarchical RNN, BiRNN + Self-Attention, ELMo + BiRNN, Fine-tuned BERT |
4e50e9965059899d15d3c3a0c0a2d73e0c5802a0 | 4e50e9965059899d15d3c3a0c0a2d73e0c5802a0_0 | Q: What is the size of this dataset?
Text: Introduction
Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.
Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweet... | 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words |
67d8e50ddcc870db71c94ad0ad7f8a59a6c67ca6 | 67d8e50ddcc870db71c94ad0ad7f8a59a6c67ca6_0 | Q: How many annotators were there?
Text: Introduction
Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.
Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets ... | 3 |
aecb485ea7d501094e50ad022ade4f0c93088d80 | aecb485ea7d501094e50ad022ade4f0c93088d80_0 | Q: Can SCRF be used to pretrain the model?
Text: Introduction
State-of-the-art speech recognition accuracy has significantly improved over the past few years since the application of deep neural networks BIBREF0 , BIBREF1 . Recently, it has been shown that with the application of both neural network acoustic model and ... | No |
2fea3c955ff78220b2c31a8ad1322bc77f6706f8 | 2fea3c955ff78220b2c31a8ad1322bc77f6706f8_0 | Q: What conclusions are drawn from the syntactic analysis?
Text: Introduction
A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways – for example, in some langu... | our method enables to control the morphological realization of first and second-person pronouns, together with verbs and adjectives related to them |
faa4f28a2f2968cecb770d9379ab2cfcaaf5cfab | faa4f28a2f2968cecb770d9379ab2cfcaaf5cfab_0 | Q: What type of syntactic analysis is performed?
Text: Introduction
A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways – for example, in some languages gende... | Speaker's Gender Effects, Interlocutors' Gender and Number Effects |
da068b20988883bc324e55c073fb9c1a5c39be33 | da068b20988883bc324e55c073fb9c1a5c39be33_0 | Q: How is it demonstrated that the correct gender and number information is injected using this system?
Text: Introduction
A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information i... | correct information substantially improves it - we see an increase of up to 2.3 BLEU over the baseline, Finally, the “She said” prefixes substantially increase the number of feminine-marked verbs, bringing the proportion much closer to that of the reference |
0d6d5b6c00551dd0d2519f117ea81d1e9e8785ec | 0d6d5b6c00551dd0d2519f117ea81d1e9e8785ec_0 | Q: Which neural machine translation system is used?
Text: Introduction
A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways – for example, in some languages ge... | Google's machine translation system (GMT) |
edcde2b675cf8a362a63940b2bbdf02c150fe01f | edcde2b675cf8a362a63940b2bbdf02c150fe01f_0 | Q: What are the components of the black-box context injection system?
Text: Introduction
A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways – for example, in... | supply an NMT system with knowledge regarding the speaker and interlocutor of first-person sentences |
d20d6c8ecd7cb0126479305d27deb0c8b642b09f | d20d6c8ecd7cb0126479305d27deb0c8b642b09f_0 | Q: What normalization techniques are mentioned?
Text: Introduction
Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF... | FBanks with cepstral mean normalization (CMN), variance with mean normalization (CMVN) |
11e6b79f1f48ddc6c580c4d0a3cb9bcb42decb17 | 11e6b79f1f48ddc6c580c4d0a3cb9bcb42decb17_0 | Q: What features do they experiment with?
Text: Introduction
Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF0 , BI... | 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window, deltas and delta-deltas (120 features in vector), spectrogram |
2677b88c2def3ed94e25a776599555a788d197f2 | 2677b88c2def3ed94e25a776599555a788d197f2_0 | Q: Which architecture is their best model?
Text: Introduction
Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF0 , B... | 6-layer bLSTM with 1024 hidden units |
8ca31caa34cc5b65dc1d01d0d1f36bf8c4928805 | 8ca31caa34cc5b65dc1d01d0d1f36bf8c4928805_0 | Q: What kind of spontaneous speech is used?
Text: Introduction
Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF0 , ... | Unanswerable |
9ab43f941c11a4b09a0e4aea61b4a5b4612e7933 | 9ab43f941c11a4b09a0e4aea61b4a5b4612e7933_0 | Q: What approach did previous models use for multi-span questions?
Text: Introduction
The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performance ... | Only MTMSM specifically tried to tackle the multi-span questions. Their approach consisted of two parts: first train a dedicated categorical variable to predict the number of spans to extract and the second was to generalize the single-span head method of extracting a span |
5a02a3dd26485a4e4a77411b50b902d2bda3731b | 5a02a3dd26485a4e4a77411b50b902d2bda3731b_0 | Q: How they use sequence tagging to answer multi-span questions?
Text: Introduction
The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performance on... | To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span |
579941de2838502027716bae88e33e79e69997a6 | 579941de2838502027716bae88e33e79e69997a6_0 | Q: What is difference in peformance between proposed model and state-of-the art on other question types?
Text: Introduction
The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. W... | For single-span questions, the proposed LARGE-SQUAD improve performance of the MTMSNlarge baseline for 2.1 EM and 1.55 F1.
For number type question, MTMSNlarge baseline have improvement over LARGE-SQUAD for 3,11 EM and 2,98 F1.
For date question, LARGE-SQUAD have improvements in 2,02 EM but MTMSNlarge have improv... |
9a65cfff4d99e4f9546c72dece2520cae6231810 | 9a65cfff4d99e4f9546c72dece2520cae6231810_0 | Q: What is the performance of proposed model on entire DROP dataset?
Text: Introduction
The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performanc... | The proposed model achieves EM 77,63 and F1 80,73 on the test and EM 76,95 and F1 80,25 on the dev |
a9def7958eac7b9a780403d4f136927f756bab83 | a9def7958eac7b9a780403d4f136927f756bab83_0 | Q: What is the previous model that attempted to tackle multi-span questions as a part of its design?
Text: Introduction
The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With ... | MTMSN BIBREF4 |
547be35cff38028648d199ad39fb48236cfb99ee | 547be35cff38028648d199ad39fb48236cfb99ee_0 | Q: How much more data does the model trained using XR loss have access to, compared to the fully supervised model?
Text: Introduction
Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require l... | Unanswerable |
47a30eb4d0d6f5f2ff4cdf6487265a25c1b18fd8 | 47a30eb4d0d6f5f2ff4cdf6487265a25c1b18fd8_0 | Q: Does the system trained only using XR loss outperform the fully supervised neural system?
Text: Introduction
Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work ... | Yes |
e42fbf6c183abf1c6c2321957359c7683122b48e | e42fbf6c183abf1c6c2321957359c7683122b48e_0 | Q: How accurate is the aspect based sentiment classifier trained only using the XR loss?
Text: Introduction
Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well... | BiLSTM-XR-Dev Estimation accuracy is 83.31 for SemEval-15 and 87.68 for SemEval-16.
BiLSTM-XR accuracy is 83.31 for SemEval-15 and 88.12 for SemEval-16.
|
e574f0f733fb98ecef3c64044004aa7a320439be | e574f0f733fb98ecef3c64044004aa7a320439be_0 | Q: How is the expectation regularization loss defined?
Text: Introduction
Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. However, many tasks require manu... | DISPLAYFORM0 |
b65b1c366c8bcf544f1be5710ae1efc6d2b1e2f1 | b65b1c366c8bcf544f1be5710ae1efc6d2b1e2f1_0 | Q: What were the non-neural baselines used for the task?
Text: Introduction
While producing a sentence, humans combine various types of knowledge to produce fluent output—various shades of meaning are expressed through word selection and tone, while the language is made to conform to underlying structural rules via syn... | The Lemming model in BIBREF17 |
bd3ccb63fd8ce5575338d7332e96def7a3fabad6 | bd3ccb63fd8ce5575338d7332e96def7a3fabad6_0 | Q: Which publicly available NLU dataset is used?
Text: Introduction
Research in Conversational AI (also known as Spoken Dialogue Systems) has applications ranging from home devices to robotics, and has a growing presence in industry. A key problem in real-world Dialogue Systems is Natural Language Understanding (NLU) –... | ROMULUS dataset, NLU-Benchmark dataset |
7c794fa0b2818d354ca666969107818a2ffdda0c | 7c794fa0b2818d354ca666969107818a2ffdda0c_0 | Q: What metrics other than entity tagging are compared?
Text: Introduction
Research in Conversational AI (also known as Spoken Dialogue Systems) has applications ranging from home devices to robotics, and has a growing presence in industry. A key problem in real-world Dialogue Systems is Natural Language Understanding ... | We also report the metrics in BIBREF7 for consistency, we report the span F1, Exact Match (EM) accuracy of the entire sequence of labels, metric that combines intent and entities |
1ef5fc4473105f1c72b4d35cf93d312736833d3d | 1ef5fc4473105f1c72b4d35cf93d312736833d3d_0 | Q: Do they provide decision sequences as supervision while training models?
Text: Introduction
Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these... | No |
5f9bd99a598a4bbeb9d2ac46082bd3302e961a0f | 5f9bd99a598a4bbeb9d2ac46082bd3302e961a0f_0 | Q: What are the models evaluated on?
Text: Introduction
Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to read ... | They evaluate F1 score and agent's test performance on their own built interactive datasets (iSQuAD and iNewsQA) |
b2fab9ffbcf1d6ec6d18a05aeb6e3ab9a4dbf2ae | b2fab9ffbcf1d6ec6d18a05aeb6e3ab9a4dbf2ae_0 | Q: How do they train models in this setup?
Text: Introduction
Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to... | Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL). |
e9cf1b91f06baec79eb6ddfd91fc5d434889f652 | e9cf1b91f06baec79eb6ddfd91fc5d434889f652_0 | Q: What commands does their setup provide to models seeking information?
Text: Introduction
Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these da... | previous, next, Ctrl+F $<$query$>$, stop |
6976296126e4a5c518e6b57de70f8dc8d8fde292 | 6976296126e4a5c518e6b57de70f8dc8d8fde292_0 | Q: What models do they propose?
Text: Introduction
Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal lite... | Feature Concatenation Model (FCM), Spatial Concatenation Model (SCM), Textual Kernels Model (TKM) |
53640834d68cf3b86cf735ca31f1c70aa0006b72 | 53640834d68cf3b86cf735ca31f1c70aa0006b72_0 | Q: Are all tweets in English?
Text: Introduction
Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal litera... | Unanswerable |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.