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id stringlengths 40 40	pid stringlengths 42 42	input stringlengths 8.37k 169k	output stringlengths 1 1.63k
c383fa9170ae00a4a24a8e39358c38395c5f034b	c383fa9170ae00a4a24a8e39358c38395c5f034b_0	Q: How they know what are content words? Text: Introduction All text has style, whether it be formal or informal, polite or aggressive, colloquial, persuasive, or even robotic. Despite the success of style transfer in image processing BIBREF0, BIBREF1, there has been limited progress in the text domain, where disentang...	words found in the control word lists are then removed, The remaining words, which represent the content
83251fd4a641cea8b180b49027e74920bca2699a	83251fd4a641cea8b180b49027e74920bca2699a_0	Q: How they model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions? Text: Introduction All text has style, whether it be formal or informal, polite or aggressive, colloquial, persuasive, or even robotic. Despite the success of style tra...	style of a sentence is represented as a vector of counts of closed word classes (like personal pronouns) as well as counts of syntactic features like the number of SBAR non-terminals in its constituency parse, since clause structure has been shown to be indicative of style
5d70c32137e82943526911ebdf78694899b3c28a	5d70c32137e82943526911ebdf78694899b3c28a_0	Q: Do they report results only on English data? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State Un...	Unanswerable
97dac7092cf8082a6238aaa35f4b185343b914af	97dac7092cf8082a6238aaa35f4b185343b914af_0	Q: What insights into the relationship between demographics and mental health are provided? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Com...	either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age, more women than men were given a diagnosis of depression
195611926760d1ceec00bd043dfdc8eba2df5ad1	195611926760d1ceec00bd043dfdc8eba2df5ad1_0	Q: What model is used to achieve 5% improvement on F1 for identifying depressed individuals on Twitter? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Depar...	Random Forest classifier
445e792ce7e699e960e2cb4fe217aeacdd88d392	445e792ce7e699e960e2cb4fe217aeacdd88d392_0	Q: How do this framework facilitate demographic inference from social media? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science &...	Demographic information is predicted using weighted lexicon of terms.
a3b1520e3da29d64af2b6e22ff15d330026d0b36	a3b1520e3da29d64af2b6e22ff15d330026d0b36_0	Q: What types of features are used from each data type? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright ...	facial presence, Facial Expression, General Image Features, textual content, analytical thinking, clout, authenticity, emotional tone, Sixltr, informal language markers, 1st person singular pronouns
2cf8825639164a842c3172af039ff079a8448592	2cf8825639164a842c3172af039ff079a8448592_0	Q: How is the data annotated? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State University, OH, USA ...	The data are self-reported by Twitter users and then verified by two human experts.
36b25021464a9574bf449e52ae50810c4ac7b642	36b25021464a9574bf449e52ae50810c4ac7b642_0	Q: Where does the information on individual-level demographics come from? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & En...	From Twitter profile descriptions of the users.
98515bd97e4fae6bfce2d164659cd75e87a9fc89	98515bd97e4fae6bfce2d164659cd75e87a9fc89_0	Q: What is the source of the user interaction data? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright Sta...	Sociability from ego-network on Twitter
53bf6238baa29a10f4ff91656c470609c16320e1	53bf6238baa29a10f4ff91656c470609c16320e1_0	Q: What is the source of the textual data? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State Univer...	Users' tweets
b27f7993b1fe7804c5660d1a33655e424cea8d10	b27f7993b1fe7804c5660d1a33655e424cea8d10_0	Q: What is the source of the visual data? Text: 0pt00 0pt00 0pt00 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State Univers...	Profile pictures from the Twitter users' profiles.
e21a8581cc858483a31c6133e53dd0cfda76ae4c	e21a8581cc858483a31c6133e53dd0cfda76ae4c_0	Q: Is there an online demo of their system? Text: Introduction Chinese definition modeling is the task of generating a definition in Chinese for a given Chinese word. This task can benefit the compilation of dictionaries, especially dictionaries for Chinese as a foreign language (CFL) learners. In recent years, the num...	No
9f6e877e3bde771595e8aee10c2656a0e7b9aeb2	9f6e877e3bde771595e8aee10c2656a0e7b9aeb2_0	Q: Do they perform manual evaluation? Text: Introduction Chinese definition modeling is the task of generating a definition in Chinese for a given Chinese word. This task can benefit the compilation of dictionaries, especially dictionaries for Chinese as a foreign language (CFL) learners. In recent years, the number of...	Yes
a3783e42c2bf616c8a07bd3b3d503886660e4344	a3783e42c2bf616c8a07bd3b3d503886660e4344_0	Q: Do they compare against Noraset et al. 2017? Text: Introduction Chinese definition modeling is the task of generating a definition in Chinese for a given Chinese word. This task can benefit the compilation of dictionaries, especially dictionaries for Chinese as a foreign language (CFL) learners. In recent years, the...	Yes
0d0959dba3f7c15ee4f5cdee51682656c4abbd8f	0d0959dba3f7c15ee4f5cdee51682656c4abbd8f_0	Q: What is a sememe? Text: Introduction Chinese definition modeling is the task of generating a definition in Chinese for a given Chinese word. This task can benefit the compilation of dictionaries, especially dictionaries for Chinese as a foreign language (CFL) learners. In recent years, the number of CFL learners has...	Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed of several sememes
589be705a5cc73a23f30decba23ce58ec39d313b	589be705a5cc73a23f30decba23ce58ec39d313b_0	Q: What data did they use? Text: Introduction The advent of neural networks in natural language processing (NLP) has significantly improved state-of-the-art results within the field. While recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) initially dominated the field, recent models started i...	the Dutch section of the OSCAR corpus
6e962f1f23061f738f651177346b38fd440ff480	6e962f1f23061f738f651177346b38fd440ff480_0	Q: What is the state of the art? Text: Introduction The advent of neural networks in natural language processing (NLP) has significantly improved state-of-the-art results within the field. While recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) initially dominated the field, recent models sta...	BERTje BIBREF8, an ULMFiT model (Universal Language Model Fine-tuning for Text Classification model) BIBREF19., mBERT
594a6bf37eab64a16c6a05c365acc100e38fcff1	594a6bf37eab64a16c6a05c365acc100e38fcff1_0	Q: What language tasks did they experiment on? Text: Introduction The advent of neural networks in natural language processing (NLP) has significantly improved state-of-the-art results within the field. While recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) initially dominated the field, rec...	sentiment analysis, the disambiguation of demonstrative pronouns,
d79d897f94e666d5a6fcda3b0c7e807c8fad109e	d79d897f94e666d5a6fcda3b0c7e807c8fad109e_0	Q: What result from experiments suggest that natural language based agents are more robust? Text: Introduction “The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations." (Edward Sapir, Language: An Intro...	Average reward across 5 seeds show that NLP representations are robust to changes in the environment as well task-nuisances
599d9ca21bbe2dbe95b08cf44dfc7537bde06f98	599d9ca21bbe2dbe95b08cf44dfc7537bde06f98_0	Q: How better is performance of natural language based agents in experiments? Text: Introduction “The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations." (Edward Sapir, Language: An Introduction to the...	Unanswerable
827464c79f33e69959de619958ade2df6f65fdee	827464c79f33e69959de619958ade2df6f65fdee_0	Q: How much faster natural language agents converge in performed experiments? Text: Introduction “The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations." (Edward Sapir, Language: An Introduction to the...	Unanswerable
8e857e44e4233193c7b2d538e520d37be3ae1552	8e857e44e4233193c7b2d538e520d37be3ae1552_0	Q: What experiments authors perform? Text: Introduction “The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations." (Edward Sapir, Language: An Introduction to the Study of Speech, 1921) Deep Learning bas...	a basic scenario, a health gathering scenario, a scenario in which the agent must take cover from fireballs, a scenario in which the agent must defend itself from charging enemies, and a super scenario, where a mixture of the above scenarios
084fb7c80a24b341093d4bf968120e3aff56f693	084fb7c80a24b341093d4bf968120e3aff56f693_0	Q: How is state to learn and complete tasks represented via natural language? Text: Introduction “The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations." (Edward Sapir, Language: An Introduction to the...	represent the state using natural language
babe72f0491e65beff0e5889380e8e32d7a81f78	babe72f0491e65beff0e5889380e8e32d7a81f78_0	Q: How does the model compare with the MMR baseline? Text: Introduction The development of automatic tools for the summarization of large corpora of documents has attracted a widespread interest in recent years. With fields of application ranging from medical sciences to finance and legal science, these summarization s...	Moreover, our TL-TranSum method also outperforms other approaches such as MaxCover ( $5\%$ ) and MRMR ( $7\%$ )
31ee92e521be110b6a5a8d08cc9e6f90a3a97aae	31ee92e521be110b6a5a8d08cc9e6f90a3a97aae_0	Q: Does the paper discuss previous models which have been applied to the same task? Text: Moral sentiment change and language People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How ...	Yes
737397f66751624bcf4ef891a10b29cfc46b0520	737397f66751624bcf4ef891a10b29cfc46b0520_0	Q: Which datasets are used in the paper? Text: Moral sentiment change and language People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a lon...	Google N-grams COHA Moral Foundations Dictionary (MFD)
87cb19e453cf7e248f24b5f7d1ff9f02d87fc261	87cb19e453cf7e248f24b5f7d1ff9f02d87fc261_0	Q: How does the parameter-free model work? Text: Moral sentiment change and language People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a l...	A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule;, A Naïve Bayes model considers both mean and variance, under the assumption of independence among embedding d...
5fb6a21d10adf4e81482bb5c1ec1787dc9de260d	5fb6a21d10adf4e81482bb5c1ec1787dc9de260d_0	Q: How do they quantify moral relevance? Text: Moral sentiment change and language People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a lon...	By complementing morally relevant seed words with a set of morally irrelevant seed words based on the notion of valence
542a87f856cb2c934072bacaa495f3c2645f93be	542a87f856cb2c934072bacaa495f3c2645f93be_0	Q: Which fine-grained moral dimension examples do they showcase? Text: Moral sentiment change and language People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral vie...	Care / Harm, Fairness / Cheating, Loyalty / Betrayal, Authority / Subversion, and Sanctity / Degradation
4fcc668eb3a042f60c4ce2e7d008e7923b25b4fc	4fcc668eb3a042f60c4ce2e7d008e7923b25b4fc_0	Q: Which dataset sources to they use to demonstrate moral sentiment through history? Text: Moral sentiment change and language People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How...	Unanswerable
c180f44667505ec03214d44f4970c0db487a8bae	c180f44667505ec03214d44f4970c0db487a8bae_0	Q: How well did the system do? Text: Introduction Interactive fictions—also called text-adventure games or text-based games—are games in which a player interacts with a virtual world purely through textual natural language—receiving descriptions of what they “see” and writing out how they want to act, an example can be...	the neural approach is generally preferred by a greater percentage of participants than the rules or random, human-made game outperforms them all
76d62e414a345fe955dc2d99562ef5772130bc7e	76d62e414a345fe955dc2d99562ef5772130bc7e_0	Q: How is the information extracted? Text: Introduction Interactive fictions—also called text-adventure games or text-based games—are games in which a player interacts with a virtual world purely through textual natural language—receiving descriptions of what they “see” and writing out how they want to act, an example ...	neural question-answering technique to extract relations from a story text, OpenIE5, a commonly used rule-based information extraction technique
6b9310b577c6232e3614a1612cbbbb17067b3886	6b9310b577c6232e3614a1612cbbbb17067b3886_0	Q: What are some guidelines in writing input vernacular so model can generate Text: Introduction During thousands of years, millions of classical Chinese poems have been written. They contain ancient poets' emotions such as their appreciation for nature, desiring for freedom and concerns for their countries. Among var...	if a vernacular paragraph contains more poetic images used in classical literature, its generated poem usually achieves higher score, poems generated from descriptive paragraphs achieve higher scores than from logical or philosophical paragraphs
d484a71e23d128f146182dccc30001df35cdf93f	d484a71e23d128f146182dccc30001df35cdf93f_0	Q: How much is proposed model better in perplexity and BLEU score than typical UMT models? Text: Introduction During thousands of years, millions of classical Chinese poems have been written. They contain ancient poets' emotions such as their appreciation for nature, desiring for freedom and concerns for their countrie...	Perplexity of the best model is 65.58 compared to best baseline 105.79. Bleu of the best model is 6.57 compared to best baseline 5.50.
5787ac3e80840fe4cf7bfae7e8983fa6644d6220	5787ac3e80840fe4cf7bfae7e8983fa6644d6220_0	Q: What dataset is used for training? Text: Introduction During thousands of years, millions of classical Chinese poems have been written. They contain ancient poets' emotions such as their appreciation for nature, desiring for freedom and concerns for their countries. Among various types of classical poetry, quatrain ...	We collected a corpus of poems and a corpus of vernacular literature from online resources
ee31c8a94e07b3207ca28caef3fbaf9a38d94964	ee31c8a94e07b3207ca28caef3fbaf9a38d94964_0	Q: What were the evaluation metrics? Text: Introduction Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, B...	BLEU, Micro Entity F1, quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5
66d743b735ba75589486e6af073e955b6bb9d2a4	66d743b735ba75589486e6af073e955b6bb9d2a4_0	Q: What were the baseline systems? Text: Introduction Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIB...	Attn seq2seq, Ptr-UNK, KV Net, Mem2Seq, DSR
b9f852256113ef468d60e95912800fab604966f6	b9f852256113ef468d60e95912800fab604966f6_0	Q: Which dialog datasets did they experiment with? Text: Introduction Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBRE...	Camrest, InCar Assistant
88f8ab2a417eae497338514142ac12c3cec20876	88f8ab2a417eae497338514142ac12c3cec20876_0	Q: What KB is used? Text: Introduction Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, several w...	Unanswerable