context stringlengths 3.85k 99.8k | questions sequencelengths 1 12 | answers sequencelengths 1 12 |
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Although Neural Machine Translation (NMT) has dominated recent research on translation tasks BIBREF0, BIBREF1, BIBREF2, NMT heavily relies on large-scale parallel data, resulting in poor performance on low-resource or zero-resource language pairs BIBREF3. Translation between these low-resource languages (e.g., Arabic$\... | [
"which multilingual approaches do they compare with?",
"what are the pivot-based baselines?",
"which datasets did they experiment with?",
"what language pairs are explored?"
] | [
[
"",
""
],
[
"",
""
],
[
"",
""
],
[
"De-En, En-Fr, Fr-En, En-Es, Ro-En, En-De, Ar-En, En-Ru",
""
]
] |
Named entity recognition is an important task of natural language processing, featuring in many popular text processing toolkits. This area of natural language processing has been actively studied in the latest decades and the advent of deep learning reinvigorated the research on more effective and accurate models. How... | [
"what ner models were evaluated?",
"what is the source of the news sentences?",
"did they use a crowdsourcing platform for manual annotations?"
] | [
[
"",
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],
[
"",
""
],
[
"",
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“I'm supposed to trust the opinion of a MS minion? The people that produced Windows ME, Vista and 8? They don't even understand people, yet they think they can predict the behavior of new, self-guiding AI?” –anonymous“I think an AI would make it easier for Patients to confide their information because by nature, a robo... | [
"what are the topics pulled from Reddit?",
"What predictive model do they build?"
] | [
[
"",
"training data has posts from politics, business, science and other popular topics; the trained model is applied to millions of unannotated posts on all of Reddit"
],
[
"",
""
]
] |
There has been significant progress on Named Entity Recognition (NER) in recent years using models based on machine learning algorithms BIBREF0 , BIBREF1 , BIBREF2 . As with other Natural Language Processing (NLP) tasks, building NER systems typically requires a massive amount of labeled training data which are annotat... | [
"What accuracy does the proposed system achieve?",
"What crowdsourcing platform is used?"
] | [
[
"F1 scores of 85.99 on the DL-PS data, 75.15 on the EC-MT data and 71.53 on the EC-UQ data ",
"F1 of 85.99 on the DL-PS dataset (dialog domain); 75.15 on EC-MT and 71.53 on EC-UQ (e-commerce domain)"
],
[
"",
"They did not use any platform, instead they hired undergraduate students to do the ... |
Deep Learning approaches have achieved impressive results on various NLP tasks BIBREF0 , BIBREF1 , BIBREF2 and have become the de facto approach for any NLP task. However, these deep learning techniques have found to be less effective for low-resource languages when the available training data is very less BIBREF3 . Re... | [
"How do they match words before reordering them?",
"On how many language pairs do they show that preordering assisting language sentences helps translation quality?",
"Which dataset(s) do they experiment with?"
] | [
[
"",
""
],
[
"5",
""
],
[
"",
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]
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Simplified language is a variety of standard language characterized by reduced lexical and syntactic complexity, the addition of explanations for difficult concepts, and clearly structured layout. Among the target groups of simplified language commonly mentioned are persons with cognitive impairment or learning disabil... | [
"Which information about text structure is included in the corpus?",
"Which information about typography is included in the corpus?"
] | [
[
"",
"paragraph, lines, textspan element (paragraph segmentation, line segmentation, Information on physical page segmentation(for PDF only))"
],
[
"",
""
]
] |
Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB. Figure 1 illustrates the proc... | [
"On which benchmarks they achieve the state of the art?",
"What they use in their propsoed framework?",
"What does KBQA abbreviate for",
"What is te core component for KBQA?"
] | [
[
"",
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],
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"",
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],
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"",
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[
"",
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The application of deep learning methods to NLP is made possible by representing words as vectors in a low-dimensional continuous space. Traditionally, these word embeddings were static: each word had a single vector, regardless of context BIBREF0, BIBREF1. This posed several problems, most notably that all senses of a... | [
"What experiments are proposed to test that upper layers produce context-specific embeddings?",
"How do they calculate a static embedding for each word?"
] | [
[
"They measure self-similarity, intra-sentence similarity and maximum explainable variance of the embeddings in the upper layers.",
"They plot the average cosine similarity between uniformly random words increases exponentially from layers 8 through 12. \nThey plot the average self-similarity of uniformly... |
"During the first two decades of the 21st century, the sharing and processing of vast amounts of dat(...TRUNCATED) | ["What is the performance of BERT on the task?","What are the other algorithms tested?","Does BERT r(...TRUNCATED) | [["F1 scores are:\nHUBES-PHI: Detection(0.965), Classification relaxed (0.95), Classification strict(...TRUNCATED) |
"Accurate grapheme-to-phoneme conversion (g2p) is important for any application that depends on the (...TRUNCATED) | ["how is model compactness measured?","what was the baseline?","what evaluation metrics were used?",(...TRUNCATED) | [
[
"Using file size on disk",
""
],
[
"",
""
],
[
"",
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],
[
"",
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]
] |
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