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id stringlengths 40 40	pid stringlengths 42 42	input stringlengths 8.37k 169k	output stringlengths 1 1.63k
f27502c3ece9ade265389d5ace90ca9ca42b46f3	f27502c3ece9ade265389d5ace90ca9ca42b46f3_0	Q: How do they evaluate generated stories? Text: Introduction Collaborative human-machine story-writing has had a recent resurgence of attention from the research community BIBREF0 , BIBREF1 . It represents a frontier for AI research; as a research community we have developed convincing NLP systems for some generative ...	separate set of Turkers to rate the stories for overall quality and the three improvement areas
ffb7a12dfe069ab7263bb7dd366817a9d22b8ef2	ffb7a12dfe069ab7263bb7dd366817a9d22b8ef2_0	Q: Do they evaluate in other language appart from English? Text: Introduction Collaborative human-machine story-writing has had a recent resurgence of attention from the research community BIBREF0 , BIBREF1 . It represents a frontier for AI research; as a research community we have developed convincing NLP systems for ...	Unanswerable
aa4b38f601cc87bf93849245d5f65124da3dc112	aa4b38f601cc87bf93849245d5f65124da3dc112_0	Q: What are the baselines? Text: Introduction Collaborative human-machine story-writing has had a recent resurgence of attention from the research community BIBREF0 , BIBREF1 . It represents a frontier for AI research; as a research community we have developed convincing NLP systems for some generative tasks like machi...	Title-to-Story system
08b87a90139968095433f27fc88f571d939cd433	08b87a90139968095433f27fc88f571d939cd433_0	Q: What is used a baseline? Text: Introduction Indicators of Compromise (IOCs) are forensic artifacts that are used as signs when a system has been compromised by an attacker or infected with a particular piece of malware. To be specific, IOCs are composed of some combinations of virus signatures, IPs, URLs or domain n...	As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12
ef872807cb0c9974d18bbb886a7836e793727c3d	ef872807cb0c9974d18bbb886a7836e793727c3d_0	Q: What contextual features are used? Text: Introduction Indicators of Compromise (IOCs) are forensic artifacts that are used as signs when a system has been compromised by an attacker or infected with a particular piece of malware. To be specific, IOCs are composed of some combinations of virus signatures, IPs, URLs o...	The words that can indicate the characteristics of the neighbor words as contextual keywords and generate it from the automatically extracted contextual keywords.
4db3c2ca6ddc87209c31b20763b7a3c1c33387bc	4db3c2ca6ddc87209c31b20763b7a3c1c33387bc_0	Q: Where are the cybersecurity articles used in the model sourced from? Text: Introduction Indicators of Compromise (IOCs) are forensic artifacts that are used as signs when a system has been compromised by an attacker or infected with a particular piece of malware. To be specific, IOCs are composed of some combination...	from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018
63337fd803f6fdd060ebd0f53f9de79d451810cd	63337fd803f6fdd060ebd0f53f9de79d451810cd_0	Q: What type of hand-crafted features are used in state of the art IOC detection systems? Text: Introduction Indicators of Compromise (IOCs) are forensic artifacts that are used as signs when a system has been compromised by an attacker or infected with a particular piece of malware. To be specific, IOCs are composed o...	Unanswerable
63496705fff20c55d4b3d8cdf4786f93e742dd3d	63496705fff20c55d4b3d8cdf4786f93e742dd3d_0	Q: Do they compare DeepER against other approaches? Text: Introduction A Question Answering (QA) system is a computer program capable of understanding questions in a natural language, finding answers to them in a knowledge base and providing answers in the same language. So broadly defined task seems very hard; BIBREF0...	Yes
7b44bee49b7cb39cb7d5eec79af5773178c27d4d	7b44bee49b7cb39cb7d5eec79af5773178c27d4d_0	Q: How is the data in RAFAEL labelled? Text: Introduction A Question Answering (QA) system is a computer program capable of understanding questions in a natural language, finding answers to them in a knowledge base and providing answers in the same language. So broadly defined task seems very hard; BIBREF0 describes it...	Using a set of annotation tools such as Morfeusz, PANTERA, Spejd, NERF and Liner
6d54bad91b6ccd1108d1ddbff1d217c6806e0842	6d54bad91b6ccd1108d1ddbff1d217c6806e0842_0	Q: How do they handle polysemous words in their entity library? Text: Introduction A Question Answering (QA) system is a computer program capable of understanding questions in a natural language, finding answers to them in a knowledge base and providing answers in the same language. So broadly defined task seems very h...	only the first word sense (usually the most common) is taken into account
238ec3c1e1093ce2f5122ee60209b969f7669fae	238ec3c1e1093ce2f5122ee60209b969f7669fae_0	Q: How is the fluctuation in the sense of the word and its neighbors measured? Text: Introduction Distributed representation of word sense provides us with the ability to perform several operations on the word. One of the most important operations on a word is to obtain the set of words whose meaning is similar to the ...	Our method performs a statistical test to determine whether a given word is used polysemously in the text, according to the following steps: 1) Setting N, the size of the neighbor. 2) Choosing N neighboring words ai in the order whose angle with the vector of the given word w is the smallest. 3) Computing the surroundi...
f704d182c9e01a2002381b76bf21e4bb3c0d3efc	f704d182c9e01a2002381b76bf21e4bb3c0d3efc_0	Q: Among various transfer learning techniques, which technique yields to the best performance? Text: Introduction Question answering (QA) is the task of retrieving answers to a question given one or more contexts. It has been explored both in the open-domain setting BIBREF0 as well as domain-specific settings, such as ...	Unanswerable
da544015511e535503dee2eaf4912a5e36c806cd	da544015511e535503dee2eaf4912a5e36c806cd_0	Q: What is the architecture of the model? Text: Introduction Quickly making sense of large amounts of linguistic data is an important application of language technology. For example, after the 2011 Japanese tsunami, natural language processing was used to quickly filter social media streams for messages about the safet...	BIBREF5 to train neural sequence-to-sequence, NMF topic model with scikit-learn BIBREF14
7bc993b32484d6ae3c86d0b351a68e59fd2757a5	7bc993b32484d6ae3c86d0b351a68e59fd2757a5_0	Q: What language do they look at? Text: Introduction Quickly making sense of large amounts of linguistic data is an important application of language technology. For example, after the 2011 Japanese tsunami, natural language processing was used to quickly filter social media streams for messages about the safety of ind...	Spanish
da495e2f99ee2d5db9cc17eca5517ddaa5ea8e42	da495e2f99ee2d5db9cc17eca5517ddaa5ea8e42_0	Q: Where does the vocabulary come from? Text: Introduction Neural machine translation (NMT) proposed by Kalchbrenner and Blunsom BIBREF0 and Sutskever et al. BIBREF1 has achieved significant progress in recent years. Unlike traditional statistical machine translation(SMT) BIBREF2 , BIBREF3 , BIBREF4 which contains mult...	LDC corpus
e44a5514d7464993997212341606c2c0f3a72eb4	e44a5514d7464993997212341606c2c0f3a72eb4_0	Q: What is the worst performing translation granularity? Text: Introduction Neural machine translation (NMT) proposed by Kalchbrenner and Blunsom BIBREF0 and Sutskever et al. BIBREF1 has achieved significant progress in recent years. Unlike traditional statistical machine translation(SMT) BIBREF2 , BIBREF3 , BIBREF4 wh...	Unanswerable
310e61b9dd4d75bc1bebbcb1dae578f55807cd04	310e61b9dd4d75bc1bebbcb1dae578f55807cd04_0	Q: What dataset did they use? Text: Introduction Neural machine translation (NMT) proposed by Kalchbrenner and Blunsom BIBREF0 and Sutskever et al. BIBREF1 has achieved significant progress in recent years. Unlike traditional statistical machine translation(SMT) BIBREF2 , BIBREF3 , BIBREF4 which contains multiple separ...	LDC corpus, NIST 2003(MT03), NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06), NIST 2008(MT08)
bdc6664cec2b94b0b3769bc70a60914795f39574	bdc6664cec2b94b0b3769bc70a60914795f39574_0	Q: How do they measure performance? Text: INTRODUCTION The Semantic Web provides a large number of structured datasets in form of Linked Data. One central obstacle is to make this data available and consumable to lay users without knowledge of formal query languages such as SPARQL. In order to satisfy specific informat...	average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values
e40df8c685a28b98006c47808f506def68f30e26	e40df8c685a28b98006c47808f506def68f30e26_0	Q: Do they measure the performance of a combined approach? Text: INTRODUCTION The Semantic Web provides a large number of structured datasets in form of Linked Data. One central obstacle is to make this data available and consumable to lay users without knowledge of formal query languages such as SPARQL. In order to sa...	Unanswerable
9653c89a93ac5c717a0a26cf80e9aa98a5ccf910	9653c89a93ac5c717a0a26cf80e9aa98a5ccf910_0	Q: Which four QA systems do they use? Text: INTRODUCTION The Semantic Web provides a large number of structured datasets in form of Linked Data. One central obstacle is to make this data available and consumable to lay users without knowledge of formal query languages such as SPARQL. In order to satisfy specific inform...	WDAqua BIBREF0 , QAKiS BIBREF7 , gAnswer BIBREF6 and Platypus BIBREF8
b921a1771ed0ba9dbeff9da000336ecf2bb38322	b921a1771ed0ba9dbeff9da000336ecf2bb38322_0	Q: How many iterations of visual search are done on average until an answer is found? Text: INTRODUCTION The Semantic Web provides a large number of structured datasets in form of Linked Data. One central obstacle is to make this data available and consumable to lay users without knowledge of formal query languages suc...	Unanswerable
412aff0b2113b7d61c914edf90b90f2994390088	412aff0b2113b7d61c914edf90b90f2994390088_0	Q: Do they test performance of their approaches using human judgements? Text: INTRODUCTION The Semantic Web provides a large number of structured datasets in form of Linked Data. One central obstacle is to make this data available and consumable to lay users without knowledge of formal query languages such as SPARQL. I...	Yes
010e3793eb1342225857d3f95e147d8f8467192a	010e3793eb1342225857d3f95e147d8f8467192a_0	Q: What are the sizes of both datasets? Text: Introduction Following previous research on automatic detection and correction of dt-mistakes in Dutch BIBREF0, this paper investigates another stumbling block for both native and non-native speakers of Dutch: the correct use of die and dat. The multiplicity of syntactic fu...	The Dutch section consists of 2,333,816 sentences and 53,487,257 words., The SONAR500 corpus consists of more than 500 million words obtained from different domains.
c20bb0847ced490a793657fbaf6afb5ef54dad81	c20bb0847ced490a793657fbaf6afb5ef54dad81_0	Q: Why are the scores for predicting perceived musical hardness and darkness extracted only for subsample of 503 songs? Text: Introduction As audio and text features provide complementary layers of information on songs, a combination of both data types has been shown to improve the automatic classification of high-leve...	Unanswerable
ff8557d93704120b65d9b597a4fab40b49d24b6d	ff8557d93704120b65d9b597a4fab40b49d24b6d_0	Q: How long is the model trained? Text: Introduction As audio and text features provide complementary layers of information on songs, a combination of both data types has been shown to improve the automatic classification of high-level attributes in music such as genre, mood and emotion BIBREF0, BIBREF1, BIBREF2, BIBRE...	Unanswerable
447eb98e602616c01187960c9c3011c62afd7c27	447eb98e602616c01187960c9c3011c62afd7c27_0	Q: What are lyrical topics present in the metal genre? Text: Introduction As audio and text features provide complementary layers of information on songs, a combination of both data types has been shown to improve the automatic classification of high-level attributes in music such as genre, mood and emotion BIBREF0, BI...	Table TABREF10 displays the twenty resulting topics
f398587b9a0008628278a5ea858e01d3f5559f65	f398587b9a0008628278a5ea858e01d3f5559f65_0	Q: By how much does SPNet outperforms state-of-the-art abstractive summarization methods on evaluation metrics? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For ...	SPNet vs best baseline: ROUGE-1: 90.97 vs 90.68 CIC: 70.45 vs 70.25
d5f8707ddc21741d52b3c2a9ab1af2871dc6c90b	d5f8707ddc21741d52b3c2a9ab1af2871dc6c90b_0	Q: What automatic and human evaluation metrics are used to compare SPNet to its counterparts? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For instance, the auto...	ROUGE and CIC, relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair
58f3bfbd01ba9768172be45a819faaa0de2ddfa4	58f3bfbd01ba9768172be45a819faaa0de2ddfa4_0	Q: Is proposed abstractive dialog summarization dataset open source? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For instance, the automatic doctor-patient inte...	Unanswerable
73633afbefa191b36cca594977204c6511f9dad4	73633afbefa191b36cca594977204c6511f9dad4_0	Q: Is it expected to have speaker role, semantic slot and dialog domain annotations in real world datasets? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For inst...	Not at the moment, but summaries can be additionaly extended with this annotations.
db39a71080e323ba2ddf958f93778e2b875dcd24	db39a71080e323ba2ddf958f93778e2b875dcd24_0	Q: How does SPNet utilize additional speaker role, semantic slot and dialog domain annotations? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For instance, the au...	Our encoder-decoder framework employs separate encoding for different speakers in the dialog., We integrate semantic slot scaffold by performing delexicalization on original dialogs., We integrate dialog domain scaffold through a multi-task framework.
6da2cb3187d3f28b75ac0a61f6562a8adf716109	6da2cb3187d3f28b75ac0a61f6562a8adf716109_0	Q: What are previous state-of-the-art document summarization methods used? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For instance, the automatic doctor-patien...	Pointer-Generator, Transformer
c47e87efab11f661993a14cf2d7506be641375e4	c47e87efab11f661993a14cf2d7506be641375e4_0	Q: How does new evaluation metric considers critical informative entities? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For instance, the automatic doctor-patien...	Answer with content missing: (formula for CIC) it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities
14684ad200915ff1e3fc2a89cb614e472a1a2854	14684ad200915ff1e3fc2a89cb614e472a1a2854_0	Q: Is new evaluation metric extension of ROGUE? Text: Introduction Summarization aims to condense a piece of text to a shorter version, retaining the critical information. On dialogs, summarization has various promising applications in the real world. For instance, the automatic doctor-patient interaction summary can s...	No
8d1f9d3aa2cc2e2e58d3da0f5edfc3047978f3ee	8d1f9d3aa2cc2e2e58d3da0f5edfc3047978f3ee_0	Q: What measures were used for human evaluation? Text: Introduction Commonsense reasoning has long been acknowledged as a critical bottleneck of artificial intelligence and especially in natural language processing. It is an ability of combining commonsense facts and logic rules to make new presumptions about ordinary ...	To have an estimation about human performance in each metric, we iteratively treat every reference sentence in dev/test data as the prediction to be compared with all references (including itself).
5065ff56d3c295b8165cb20d8bcfcf3babe9b1b8	5065ff56d3c295b8165cb20d8bcfcf3babe9b1b8_0	Q: What automatic metrics are used for this task? Text: Introduction Commonsense reasoning has long been acknowledged as a critical bottleneck of artificial intelligence and especially in natural language processing. It is an ability of combining commonsense facts and logic rules to make new presumptions about ordinary...	BLEU-3/4, ROUGE-2/L, CIDEr, SPICE, BERTScore
c34a15f1d113083da431e4157aceb11266e9a1b2	c34a15f1d113083da431e4157aceb11266e9a1b2_0	Q: Are the models required to also generate rationales? Text: Introduction Commonsense reasoning has long been acknowledged as a critical bottleneck of artificial intelligence and especially in natural language processing. It is an ability of combining commonsense facts and logic rules to make new presumptions about or...	No
061682beb3dbd7c76cfa26f7ae650e548503d977	061682beb3dbd7c76cfa26f7ae650e548503d977_0	Q: Are the rationales generated after the sentences were written? Text: Introduction Commonsense reasoning has long been acknowledged as a critical bottleneck of artificial intelligence and especially in natural language processing. It is an ability of combining commonsense facts and logic rules to make new presumption...	Yes
3518d8eb84f6228407cfabaf509fd63d60351203	3518d8eb84f6228407cfabaf509fd63d60351203_0	Q: Are the sentences in the dataset written by humans who were shown the concept-sets? Text: Introduction Commonsense reasoning has long been acknowledged as a critical bottleneck of artificial intelligence and especially in natural language processing. It is an ability of combining commonsense facts and logic rules to...	Yes
617c77a600be5529b3391ab0c21504cd288cc7c7	617c77a600be5529b3391ab0c21504cd288cc7c7_0	Q: Where do the concept sets come from? Text: Introduction Commonsense reasoning has long been acknowledged as a critical bottleneck of artificial intelligence and especially in natural language processing. It is an ability of combining commonsense facts and logic rules to make new presumptions about ordinary scenes in...	These concept-sets are sampled from several large corpora of image/video captions