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id stringlengths 40 40	pid stringlengths 42 42	input stringlengths 8.37k 169k	output stringlengths 1 1.63k
9a4aa0e4096c73cd2c3b1eab437c1bf24ae7bf03	9a4aa0e4096c73cd2c3b1eab437c1bf24ae7bf03_0	Q: What text sequences are associated with each vertex? Text: Introduction Networks are ubiquitous, with prominent examples including social networks (e.g., Facebook, Twitter) or citation networks of research papers (e.g., arXiv). When analyzing data from these real-world networks, traditional methods often represent v...	abstracts, sentences
1d1ab5d8a24dfd15d95a5a7506ac0456d1192209	1d1ab5d8a24dfd15d95a5a7506ac0456d1192209_0	Q: How long does it take for the model to run? Text: Introduction Networks are ubiquitous, with prominent examples including social networks (e.g., Facebook, Twitter) or citation networks of research papers (e.g., arXiv). When analyzing data from these real-world networks, traditional methods often represent vertices (...	Unanswerable
09a993756d2781a89f7ec5d7992f812d60e24232	09a993756d2781a89f7ec5d7992f812d60e24232_0	Q: Do they report results only on English data? Text: Introduction Improving unsupervised learning is of key importance for advancing machine learning methods, as to unlock access to almost unlimited amounts of data to be used as training resources. The majority of recent success stories of deep learning does not fall ...	Yes
37eba8c3cfe23778498d95a7dfddf8dfb725f8e2	37eba8c3cfe23778498d95a7dfddf8dfb725f8e2_0	Q: Which other unsupervised models are used for comparison? Text: Introduction Improving unsupervised learning is of key importance for advancing machine learning methods, as to unlock access to almost unlimited amounts of data to be used as training resources. The majority of recent success stories of deep learning do...	Sequential (Denoising) Autoencoder, TF-IDF BOW, SkipThought, FastSent, Siamese C-BOW, C-BOW, C-PHRASE, ParagraphVector
cdf1bf4b202576c39e063921f6b63dc9e4d6b1ff	cdf1bf4b202576c39e063921f6b63dc9e4d6b1ff_0	Q: What metric is used to measure performance? Text: Introduction Improving unsupervised learning is of key importance for advancing machine learning methods, as to unlock access to almost unlimited amounts of data to be used as training resources. The majority of recent success stories of deep learning does not fall i...	Accuracy and F1 score for supervised tasks, Pearson's and Spearman's correlation for unsupervised tasks
03f4e5ac5a9010191098d6d66ed9bbdfafcbd013	03f4e5ac5a9010191098d6d66ed9bbdfafcbd013_0	Q: How do the n-gram features incorporate compositionality? Text: Introduction Improving unsupervised learning is of key importance for advancing machine learning methods, as to unlock access to almost unlimited amounts of data to be used as training resources. The majority of recent success stories of deep learning do...	by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words
9a9338d0e74fd315af643335e733445031bd7656	9a9338d0e74fd315af643335e733445031bd7656_0	Q: Which dataset do they use? Text: Introduction Language models (LMs) are crucial components in many applications, such as speech recognition and machine translation. The aim of language models is to compute the probability of any given sentence INLINEFORM0 , which can be calculated as DISPLAYFORM0 The task of LMs is ...	AMI IHM meeting corpus
3103502cf07726d3eeda34f31c0bdf1fc0ae964e	3103502cf07726d3eeda34f31c0bdf1fc0ae964e_0	Q: How do Zipf and Herdan-Heap's laws differ? Text: Introduction Statistical characterization of languages has been a field of study for decadesBIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. Even simple quantities, like letter frequency, can be used to decode simple substitution cryptogramsBIBREF6, BIBREF7, BIBR...	Zipf's law describes change of word frequency rate, while Heaps-Herdan describes different word number in large texts (assumed that Hepas-Herdan is consequence of Zipf's)
aaec98481defc4c230f84a64cdcf793d89081a76	aaec98481defc4c230f84a64cdcf793d89081a76_0	Q: What was the best performing baseline? Text: Introduction The goal of text summarization task is to produce a summary from a set of documents. The summary should retain important information and be reasonably shorter than the original documents BIBREF0 . When the set of documents contains only a single document, the...	Lead-3
69b41524dc5820143e45f2f3545cd5c0a70e2922	69b41524dc5820143e45f2f3545cd5c0a70e2922_0	Q: Which approaches did they use? Text: Introduction The goal of text summarization task is to produce a summary from a set of documents. The summary should retain important information and be reasonably shorter than the original documents BIBREF0 . When the set of documents contains only a single document, the task is...	SumBasic, Lsa, LexRank, TextRank, Bayes, Hmm, MaxEnt, NeuralSum, Lead-N
72122e0bc5da1d07c0dadb3401aab2acd748424d	72122e0bc5da1d07c0dadb3401aab2acd748424d_0	Q: What is the size of the dataset? Text: Introduction The goal of text summarization task is to produce a summary from a set of documents. The summary should retain important information and be reasonably shorter than the original documents BIBREF0 . When the set of documents contains only a single document, the task ...	20K
1af4d56eeaf74460ca2c621a2ad8a5d8dbac491c	1af4d56eeaf74460ca2c621a2ad8a5d8dbac491c_0	Q: Did they use a crowdsourcing platform for the summaries? Text: Introduction The goal of text summarization task is to produce a summary from a set of documents. The summary should retain important information and be reasonably shorter than the original documents BIBREF0 . When the set of documents contains only a si...	No
3f5f74c39a560b5d916496e05641783c58af2c5d	3f5f74c39a560b5d916496e05641783c58af2c5d_0	Q: How are the synthetic examples generated? Text: Introduction In the last few years, research in natural text generation (NLG) has made significant progress, driven largely by the neural encoder-decoder paradigm BIBREF0, BIBREF1 which can tackle a wide array of tasks including translation BIBREF2, summarization BIBRE...	Random perturbation of Wikipedia sentences using mask-filling with BERT, backtranslation and randomly drop out
07f5e360e91b99aa2ed0284d7d6688335ed53778	07f5e360e91b99aa2ed0284d7d6688335ed53778_0	Q: Do they measure the number of created No-Arc long sequences? Text: Introduction Greedy transition-based parsers are popular in NLP, as they provide competitive accuracy with high efficiency. They syntactically analyze a sentence by greedily applying transitions, which read it from left to right and produce a depende...	No
11dde2be9a69a025f2fc29ce647201fb5a4df580	11dde2be9a69a025f2fc29ce647201fb5a4df580_0	Q: By how much does the new parser outperform the current state-of-the-art? Text: Introduction Greedy transition-based parsers are popular in NLP, as they provide competitive accuracy with high efficiency. They syntactically analyze a sentence by greedily applying transitions, which read it from left to right and produ...	Proposed method achieves 94.5 UAS and 92.4 LAS compared to 94.3 and 92.2 of best state-of-the -art greedy based parser. Best state-of-the art parser overall achieves 95.8 UAS and 94.6 LAS.
bcce5eef9ddc345177b3c39c469b4f8934700f80	bcce5eef9ddc345177b3c39c469b4f8934700f80_0	Q: Do they evaluate only on English datasets? Text: Introduction A cryptocurrency is a digital currency designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. They are based on decentralized sy...	Yes
d3092f78bdbe7e741932e3ddf997e8db42fa044c	d3092f78bdbe7e741932e3ddf997e8db42fa044c_0	Q: What experimental evaluation is used? Text: Introduction A cryptocurrency is a digital currency designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. They are based on decentralized systems...	root mean square error between the actual and the predicted price of Bitcoin for every minute
e2427f182d7cda24eb7197f7998a02bc80550f15	e2427f182d7cda24eb7197f7998a02bc80550f15_0	Q: How is the architecture fault-tolerant? Text: Introduction A cryptocurrency is a digital currency designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. They are based on decentralized syste...	By using Apache Spark which stores all executions in a lineage graph and recovers to the previous steady state from any fault
0457242fb2ec33446799de229ff37eaad9932f2a	0457242fb2ec33446799de229ff37eaad9932f2a_0	Q: Which elements of the platform are modular? Text: Introduction A cryptocurrency is a digital currency designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. They are based on decentralized s...	handling large volume incoming data, sentiment analysis on tweets and predictive online learning
5e997d4499b18f1ee1ef6fa145cadbc018b8dd87	5e997d4499b18f1ee1ef6fa145cadbc018b8dd87_0	Q: What is the source of memes? Text: Motivation The spread of misinformation or hate messages through social media is a central societal challenge given the unprecedented broadcast potential of these tools. While there already exist some moderation mechanisms such as crowd-sourced abuse reports and dedicated human tea...	Google Images, Reddit Memes Dataset
12c7d79d2a26af2d445229d0c8ba3ba1aab3f5b5	12c7d79d2a26af2d445229d0c8ba3ba1aab3f5b5_0	Q: Is the dataset multimodal? Text: Motivation The spread of misinformation or hate messages through social media is a central societal challenge given the unprecedented broadcast potential of these tools. While there already exist some moderation mechanisms such as crowd-sourced abuse reports and dedicated human teams...	Yes
98daaa9eaa1e1e574be336b8933b861bfd242e5e	98daaa9eaa1e1e574be336b8933b861bfd242e5e_0	Q: How is each instance of the dataset annotated? Text: Motivation The spread of misinformation or hate messages through social media is a central societal challenge given the unprecedented broadcast potential of these tools. While there already exist some moderation mechanisms such as crowd-sourced abuse reports and d...	weakly labeled into hate or non-hate memes, depending on their source
a93196fb0fb5f8202912971e14552fd7828976db	a93196fb0fb5f8202912971e14552fd7828976db_0	Q: Which dataset do they use for text modelling? Text: Introduction Variational Autoencoder (VAE) BIBREF1 is a powerful method for learning representations of high-dimensional data. However, recent attempts of applying VAEs to text modelling are still far less successful compared to its application to image and speech ...	Penn Treebank (PTB), end-to-end (E2E) text generation corpus
983c2fe7bdbf471bb8b15db858fd2cbec86b96a5	983c2fe7bdbf471bb8b15db858fd2cbec86b96a5_0	Q: Do they compare against state of the art text generation? Text: Introduction Variational Autoencoder (VAE) BIBREF1 is a powerful method for learning representations of high-dimensional data. However, recent attempts of applying VAEs to text modelling are still far less successful compared to its application to image...	Yes
a5418e4af99a2cbd6b7a2b8041388a2d01b8efb2	a5418e4af99a2cbd6b7a2b8041388a2d01b8efb2_0	Q: How do they evaluate generated text quality? Text: Introduction Variational Autoencoder (VAE) BIBREF1 is a powerful method for learning representations of high-dimensional data. However, recent attempts of applying VAEs to text modelling are still far less successful compared to its application to image and speech B...	Loss analysis. To conduct a more thorough evaluation, we further investigate model behaviours in terms of both reconstruction loss and KL loss, as shown in Figure FIGREF14. These plots were obtained based on the E2E training set using the inputless setting.
b540cd4fe9dc4394f64d5b76b0eaa4d9e30fb728	b540cd4fe9dc4394f64d5b76b0eaa4d9e30fb728_0	Q: Could you tell me more about the metrics used for performance evaluation? Text: Introduction With the growing amount of biomedical information available in textual form, there have been significant advances in the development of pre-training language representations that can be applied to a range of different tasks ...	BLUE utilizes different metrics for each of the tasks: Pearson correlation coefficient, F-1 scores, micro-averaging, and accuracy
41173179efa6186eef17c96f7cbd8acb29105b0e	41173179efa6186eef17c96f7cbd8acb29105b0e_0	Q: which tasks are used in BLUE benchmark? Text: Introduction With the growing amount of biomedical information available in textual form, there have been significant advances in the development of pre-training language representations that can be applied to a range of different tasks in the biomedical domain, such as ...	Inference task The aim of the inference task is to predict whether the premise sentence entails or contradicts the hypothesis sentence, Document multilabel classification The multilabel classification task predicts multiple labels from the texts., Relation extraction The aim of the relation extraction task is to predic...
0bd683c51a87a110b68b377e9a06f0a3e12c8da0	0bd683c51a87a110b68b377e9a06f0a3e12c8da0_0	Q: What are the tasks that this method has shown improvements? Text: Introduction Word embeddings are one of the most widely used resources in NLP, as they have proven to be of enormous importance for modeling linguistic phenomena in both supervised and unsupervised settings. In particular, the representation of words ...	bilingual dictionary induction, monolingual and cross-lingual word similarity, and cross-lingual hypernym discovery
a979749e59e6e300a453d8a8b1627f97101799de	a979749e59e6e300a453d8a8b1627f97101799de_0	Q: Why does the model improve in monolingual spaces as well? Text: Introduction Word embeddings are one of the most widely used resources in NLP, as they have proven to be of enormous importance for modeling linguistic phenomena in both supervised and unsupervised settings. In particular, the representation of words i...	because word pair similarity increases if the two words translate to similar parts of the cross-lingual embedding space
b10632eaa0ca48f86522d8ec38b1d702cb0b8c01	b10632eaa0ca48f86522d8ec38b1d702cb0b8c01_0	Q: What are the categories being extracted? Text: Introduction and Background Electronic Health Records (EHRs) are organized collections of information about individual patients. They are designed such that they can be shared across different settings for providing health care services. The Institute of Medicine commit...	Unanswerable
8fa7011e7beaa9fb4083bf7dd75d1216f9c7b2eb	8fa7011e7beaa9fb4083bf7dd75d1216f9c7b2eb_0	Q: Do the authors test their annotation projection techniques on tasks other than AMR? Text: Introduction Abstract Meaning Representation (AMR) parsing is the process of converting natural language sentences into their corresponding AMR representations BIBREF0 . An AMR is a graph with nodes representing the concepts of...	No
e0b7acf4292b71725b140f089c6850aebf2828d2	e0b7acf4292b71725b140f089c6850aebf2828d2_0	Q: How is annotation projection done when languages have different word order? Text: Introduction Abstract Meaning Representation (AMR) parsing is the process of converting natural language sentences into their corresponding AMR representations BIBREF0 . An AMR is a graph with nodes representing the concepts of the sen...	Word alignments are generated for parallel text, and aligned words are assumed to also share AMR node alignments.
b6ffa18d49e188c454188669987b0a4807ca3018	b6ffa18d49e188c454188669987b0a4807ca3018_0	Q: What is the reasoning method that is used? Text: Introduction Ontology-based knowledge bases (KBs) like DBpedia BIBREF0 are playing an increasingly important role in domains such knowledge management, data analysis and natural language understanding. Although they are very valuable resources, the usefulness and usab...	SPARQL
2b61893b22ac190c94c2cb129e86086888347079	2b61893b22ac190c94c2cb129e86086888347079_0	Q: What KB is used in this work? Text: Introduction Ontology-based knowledge bases (KBs) like DBpedia BIBREF0 are playing an increasingly important role in domains such knowledge management, data analysis and natural language understanding. Although they are very valuable resources, the usefulness and usability of such...	DBpedia
a996b6aee9be88a3db3f4127f9f77a18ed10caba	a996b6aee9be88a3db3f4127f9f77a18ed10caba_0	Q: What's the precision of the system? Text: Introduction Ontology-based knowledge bases (KBs) like DBpedia BIBREF0 are playing an increasingly important role in domains such knowledge management, data analysis and natural language understanding. Although they are very valuable resources, the usefulness and usability o...	0.8320 on semantic typing, 0.7194 on entity matching
65e2f97f2fe8eb5c2fa41cb95c02b577e8d6e5ee	65e2f97f2fe8eb5c2fa41cb95c02b577e8d6e5ee_0	Q: How did they measure effectiveness? Text: Introduction Modern speech-based assistants, such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri, enable users to complete daily tasks such as shopping, setting reminders, and playing games using voice commands. Such human-like interfaces create a rich exper...	number of dialogs that resulted in launching a skill divided by total number of dialogs
83f14af3ccca4ab9deb4c6d208f624d1e79dc7eb	83f14af3ccca4ab9deb4c6d208f624d1e79dc7eb_0	Q: Which of the two ensembles yields the best performance? Text: Introduction Imagine that you have a friend who claims to know a lot of trivia. During a quiz, you ask them about the native language of actor Jean Marais. They correctly answer French. For a moment you are impressed, until you realize that Jean is a typi...	Answer with content missing: (Table 2) CONCAT ensemble
0154d8be772193bfd70194110f125813057413a4	0154d8be772193bfd70194110f125813057413a4_0	Q: What are the two ways of ensembling BERT and E-BERT? Text: Introduction Imagine that you have a friend who claims to know a lot of trivia. During a quiz, you ask them about the native language of actor Jean Marais. They correctly answer French. For a moment you are impressed, until you realize that Jean is a typical...	mean-pooling their outputs (AVG), concatenating the entity and its name with a slash symbol (CONCAT)
e737cfe0f6cfc6d3ac6bec32231d9c893bfc3fc9	e737cfe0f6cfc6d3ac6bec32231d9c893bfc3fc9_0	Q: How is it determined that a fact is easy-to-guess? Text: Introduction Imagine that you have a friend who claims to know a lot of trivia. During a quiz, you ask them about the native language of actor Jean Marais. They correctly answer French. For a moment you are impressed, until you realize that Jean is a typical F...	filter deletes all KB triples where the correct answer (e.g., Apple) is a case-insensitive substring of the subject entity name (e.g., Apple Watch), person name filter uses cloze-style questions to elicit name associations inherent in BERT, and deletes KB triples that correlate with them
42be49b883eba268e3dbc5c3ff4631442657dcbb	42be49b883eba268e3dbc5c3ff4631442657dcbb_0	Q: How is dependency parsing empirically verified? Text: Introduction Constituent and dependency are two typical syntactic structure representation forms as shown in Figure FIGREF1, which have been well studied from both linguistic and computational perspective BIBREF0, BIBREF1. In earlier time, linguists and NLP resea...	At last, our model is evaluated on two benchmark treebanks for both constituent and dependency parsing. The empirical results show that our parser reaches new state-of-the-art for all parsing tasks.