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id stringlengths 40 40	pid stringlengths 42 42	input stringlengths 8.37k 169k	output stringlengths 1 1.63k
6548db45fc28e8a8b51f114635bad14a13eaec5b	6548db45fc28e8a8b51f114635bad14a13eaec5b_0	Q: Which GAN do they use? Text: Introduction Generative adversarial nets (GAN) (Goodfellow et al., 2014) belong to a class of generative models which are trainable and can generate artificial data examples similar to the existing ones. In a GAN model, there are two sub-models simultaneously trained: a generative model ...	We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 .
6548db45fc28e8a8b51f114635bad14a13eaec5b	6548db45fc28e8a8b51f114635bad14a13eaec5b_1	Q: Which GAN do they use? Text: Introduction Generative adversarial nets (GAN) (Goodfellow et al., 2014) belong to a class of generative models which are trainable and can generate artificial data examples similar to the existing ones. In a GAN model, there are two sub-models simultaneously trained: a generative model ...	weGAN, deGAN
4c4f76837d1329835df88b0921f4fe8bda26606f	4c4f76837d1329835df88b0921f4fe8bda26606f_0	Q: Do they evaluate grammaticality of generated text? Text: Introduction Generative adversarial nets (GAN) (Goodfellow et al., 2014) belong to a class of generative models which are trainable and can generate artificial data examples similar to the existing ones. In a GAN model, there are two sub-models simultaneously ...	No
819d2e97f54afcc7cdb3d894a072bcadfba9b747	819d2e97f54afcc7cdb3d894a072bcadfba9b747_0	Q: Which corpora do they use? Text: Introduction Generative adversarial nets (GAN) (Goodfellow et al., 2014) belong to a class of generative models which are trainable and can generate artificial data examples similar to the existing ones. In a GAN model, there are two sub-models simultaneously trained: a generative mo...	CNN, TIME, 20 Newsgroups, and Reuters-21578
637aa32a34b20b4b0f1b5dfa08ef4e0e5ed33d52	637aa32a34b20b4b0f1b5dfa08ef4e0e5ed33d52_0	Q: Do they report results only on English datasets? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete d...	Yes
4b8257cdd9a60087fa901da1f4250e7d910896df	4b8257cdd9a60087fa901da1f4250e7d910896df_0	Q: How do the authors define or exemplify 'incorrect words'? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with inc...	typos in spellings or ungrammatical words
7e161d9facd100544fa339b06f656eb2fc64ed28	7e161d9facd100544fa339b06f656eb2fc64ed28_0	Q: How many vanilla transformers do they use after applying an embedding layer? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when...	Unanswerable
abc5836c54fc2ac8465aee5a83b9c0f86c6fd6f5	abc5836c54fc2ac8465aee5a83b9c0f86c6fd6f5_0	Q: Do they test their approach on a dataset without incomplete data? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented ...	No
abc5836c54fc2ac8465aee5a83b9c0f86c6fd6f5	abc5836c54fc2ac8465aee5a83b9c0f86c6fd6f5_1	Q: Do they test their approach on a dataset without incomplete data? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented ...	No
4debd7926941f1a02266b1a7be2df8ba6e79311a	4debd7926941f1a02266b1a7be2df8ba6e79311a_0	Q: Should their approach be applied only when dealing with incomplete data? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when pre...	No
4debd7926941f1a02266b1a7be2df8ba6e79311a	4debd7926941f1a02266b1a7be2df8ba6e79311a_1	Q: Should their approach be applied only when dealing with incomplete data? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when pre...	No
3b745f086fb5849e7ce7ce2c02ccbde7cfdedda5	3b745f086fb5849e7ce7ce2c02ccbde7cfdedda5_0	Q: By how much do they outperform other models in the sentiment in intent classification tasks? Text: Introduction Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their be...	In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average
44c7c1fbac80eaea736622913d65fe6453d72828	44c7c1fbac80eaea736622913d65fe6453d72828_0	Q: What is the sample size of people used to measure user satisfaction? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of p...	34,432 user conversations
44c7c1fbac80eaea736622913d65fe6453d72828	44c7c1fbac80eaea736622913d65fe6453d72828_1	Q: What is the sample size of people used to measure user satisfaction? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of p...	34,432
3e0c9469821cb01a75e1818f2acb668d071fcf40	3e0c9469821cb01a75e1818f2acb668d071fcf40_0	Q: What are all the metrics to measure user engagement? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BI...	overall rating, mean number of turns
3e0c9469821cb01a75e1818f2acb668d071fcf40	3e0c9469821cb01a75e1818f2acb668d071fcf40_1	Q: What are all the metrics to measure user engagement? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BI...	overall rating, mean number of turns
a725246bac4625e6fe99ea236a96ccb21b5f30c6	a725246bac4625e6fe99ea236a96ccb21b5f30c6_0	Q: What the system designs introduced? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BIBREF2, BIBREF3, B...	Amazon Conversational Bot Toolkit, natural language understanding (NLU) (nlu) module, dialog manager, knowledge bases, natural language generation (NLG) (nlg) module, text to speech (TTS) (tts)
516626825e51ca1e8a3e0ac896c538c9d8a747c8	516626825e51ca1e8a3e0ac896c538c9d8a747c8_0	Q: Do they specify the model they use for Gunrock? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BIBREF2...	No
77af93200138f46bb178c02f710944a01ed86481	77af93200138f46bb178c02f710944a01ed86481_0	Q: Do they gather explicit user satisfaction data on Gunrock? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatb...	Yes
71538776757a32eee930d297f6667cd0ec2e9231	71538776757a32eee930d297f6667cd0ec2e9231_0	Q: How do they correlate user backstory queries to user satisfaction? Text: Introduction Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of pri...	modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions
7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed	7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed_0	Q: Is ROUGE their only baseline? Text: Introduction Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandings a...	No
7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed	7aa8375cdf4690fc3b9b1799b0f5a9ec1c1736ed_1	Q: Is ROUGE their only baseline? Text: Introduction Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandings a...	No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU.
3ac30bd7476d759ea5d9a5abf696d4dfc480175b	3ac30bd7476d759ea5d9a5abf696d4dfc480175b_0	Q: what language models do they use? Text: Introduction Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandin...	LSTM LMs
0e57a0983b4731eba9470ba964d131045c8c7ea7	0e57a0983b4731eba9470ba964d131045c8c7ea7_0	Q: what questions do they ask human judges? Text: Introduction Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunder...	Unanswerable
f0317e48dafe117829e88e54ed2edab24b86edb1	f0317e48dafe117829e88e54ed2edab24b86edb1_0	Q: What misbehavior is identified? Text: Introduction In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequences of words t...	if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations
f0317e48dafe117829e88e54ed2edab24b86edb1	f0317e48dafe117829e88e54ed2edab24b86edb1_1	Q: What misbehavior is identified? Text: Introduction In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequences of words t...	if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations
ec91b87c3f45df050e4e16018d2bf5b62e4ca298	ec91b87c3f45df050e4e16018d2bf5b62e4ca298_0	Q: What is the baseline used? Text: Introduction In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequences of words to tar...	Unanswerable
f129c97a81d81d32633c94111018880a7ffe16d1	f129c97a81d81d32633c94111018880a7ffe16d1_0	Q: Which attention mechanisms do they compare? Text: Introduction In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequence...	Soft attention, Hard Stochastic attention, Local Attention
100cf8b72d46da39fedfe77ec939fb44f25de77f	100cf8b72d46da39fedfe77ec939fb44f25de77f_0	Q: Which paired corpora did they use in the other experiment? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing...	dataset that contains article-comment parallel contents INLINEFORM0 , and an unpaired dataset that contains the documents (articles or comments) INLINEFORM1
100cf8b72d46da39fedfe77ec939fb44f25de77f	100cf8b72d46da39fedfe77ec939fb44f25de77f_1	Q: Which paired corpora did they use in the other experiment? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing...	Chinese dataset BIBREF0
8cc56fc44136498471754186cfa04056017b4e54	8cc56fc44136498471754186cfa04056017b4e54_0	Q: By how much does their system outperform the lexicon-based models? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, sum...	Under the retrieval evaluation setting, their proposed model + IR2 had better MRR than NVDM by 0.3769, better MR by 4.6, and better Recall@10 by 20 . Under the generative evaluation setting the proposed model + IR2 had better BLEU by 0.044 , better CIDEr by 0.033, better ROUGE by 0.032, and better METEOR by 0.029
8cc56fc44136498471754186cfa04056017b4e54	8cc56fc44136498471754186cfa04056017b4e54_1	Q: By how much does their system outperform the lexicon-based models? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, sum...	Proposed model is better than both lexical based models by significan margin in all metrics: BLEU 0.261 vs 0.250, ROUGLE 0.162 vs 0.155 etc.
5fa431b14732b3c47ab6eec373f51f2bca04f614	5fa431b14732b3c47ab6eec373f51f2bca04f614_0	Q: Which lexicon-based models did they compare with? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main...	TF-IDF, NVDM
33ccbc401b224a48fba4b167e86019ffad1787fb	33ccbc401b224a48fba4b167e86019ffad1787fb_0	Q: How many comments were used? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the op...	from 50K to 4.8M
cca74448ab0c518edd5fc53454affd67ac1a201c	cca74448ab0c518edd5fc53454affd67ac1a201c_0	Q: How many articles did they have? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining th...	198,112
b69ffec1c607bfe5aa4d39254e0770a3433a191b	b69ffec1c607bfe5aa4d39254e0770a3433a191b_0	Q: What news comment dataset was used? Text: Introduction Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining...	Chinese dataset BIBREF0
f5cf8738e8d211095bb89350ed05ee7f9997eb19	f5cf8738e8d211095bb89350ed05ee7f9997eb19_0	Q: By how much do they outperform standard BERT? Text: Introduction With ever-increasing amounts of data available, there is an increase in the need to offer tooling to speed up processing, and eventually making sense of this data. Because fully-automated tools to extract meaning from any given input to any desired lev...	up to four percentage points in accuracy
bed527bcb0dd5424e69563fba4ae7e6ea1fca26a	bed527bcb0dd5424e69563fba4ae7e6ea1fca26a_0	Q: What dataset do they use? Text: Introduction With ever-increasing amounts of data available, there is an increase in the need to offer tooling to speed up processing, and eventually making sense of this data. Because fully-automated tools to extract meaning from any given input to any desired level of detail have ye...	2019 GermEval shared task on hierarchical text classification
bed527bcb0dd5424e69563fba4ae7e6ea1fca26a	bed527bcb0dd5424e69563fba4ae7e6ea1fca26a_1	Q: What dataset do they use? Text: Introduction With ever-increasing amounts of data available, there is an increase in the need to offer tooling to speed up processing, and eventually making sense of this data. Because fully-automated tools to extract meaning from any given input to any desired level of detail have ye...	GermEval 2019 shared task
aeab5797b541850e692f11e79167928db80de1ea	aeab5797b541850e692f11e79167928db80de1ea_0	Q: How do they combine text representations with the knowledge graph embeddings? Text: Introduction With ever-increasing amounts of data available, there is an increase in the need to offer tooling to speed up processing, and eventually making sense of this data. Because fully-automated tools to extract meaning from an...	all three representations are concatenated and passed into a MLP