Papers › Machine Comprehension Using Match-LSTM and Answer Pointer

Machine Comprehension Using Match-LSTM and Answer Pointer

29 Aug 2016arXiv:1608.07905archive 2025-07-28

Shuohang Wang, Jing Jiang

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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Code

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shuohangwang/SeqMatchSeq officialmentioned in papermentioned on GitHubtorch report
HKUST-KnowComp/MnemonicReader mentioned on GitHubpytorch report
HKUST-KnowComp/R-Net mentioned on GitHubtf report
baidu/DuReader mentioned on GitHubtf report
geraltofrivia/match-lstm-ptr-network mentioned on GitHubpytorchUnlicense report

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Tasks

Natural Language InferenceQuestion AnsweringReading Comprehension

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering SQuAD1.1 Match-LSTM with Ans-Ptr (Boundary) (ensemble) EM 67.901 #171 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Match-LSTM with Ans-Ptr (Boundary) (ensemble) F1 77.022 #171 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Match-LSTM with Bi-Ans-Ptr (Boundary) EM 64.744 #181 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Match-LSTM with Bi-Ans-Ptr (Boundary) F1 73.743 #181 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Match-LSTM with Ans-Ptr (Boundary) EM 60.474 #191 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Match-LSTM with Ans-Ptr (Boundary) F1 70.695 #191 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Match-LSTM with Ans-Ptr (Sentence) EM 54.505 #195 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Match-LSTM with Ans-Ptr (Sentence) F1 67.748 #195 of 213 Archive leaderboard report
Question Answering SQuAD1.1 dev Match-LSTM with Bi-Ans-Ptr (Boundary+Search+b) EM 64.1 #47 of 55 Archive leaderboard report
Question Answering SQuAD1.1 dev Match-LSTM with Bi-Ans-Ptr (Boundary+Search+b) F1 64.7 #47 of 55 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Logistic Regression

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