SQuAD is a large-scale reading comprehension dataset with 100k+ questions on Wikipedia passages, where answers are text segments, accompanied by a 51% F1 baseline model and 86.8% human performance.
Machine Comprehension Using Match-LSTM and Answer Pointer
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, in SQuAD the answers do not come from a small set of candidate answers and they have variable lengths. We propose an end-to-end neural architecture for the task. The architecture is based on match-LSTM, a model we proposed previously for textual entailment, and Pointer Net, a sequence-to-sequence model proposed by Vinyals et al.(2015) to constrain the output tokens to be from the input sequences. We propose two ways of using Pointer Net for our task. Our experiments show that both of our two models substantially outperform the best results obtained by Rajpurkar et al.(2016) using logistic regression and manually crafted features.
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End-to-end ASR model with speaker-specific cross-attention for two-party conversations outperforms standard models on the Switchboard corpus.
A 2019 survey of machine reading comprehension corpora and methods.
citing papers explorer
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
SQuAD is a large-scale reading comprehension dataset with 100k+ questions on Wikipedia passages, where answers are text segments, accompanied by a 51% F1 baseline model and 86.8% human performance.
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Cross-Attention End-to-End ASR for Two-Party Conversations
End-to-end ASR model with speaker-specific cross-attention for two-party conversations outperforms standard models on the Switchboard corpus.
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Machine Reading Comprehension: a Literature Review
A 2019 survey of machine reading comprehension corpora and methods.