Papers › Smarnet: Teaching Machines to Read and Comprehend Like Human
Smarnet: Teaching Machines to Read and Comprehend Like Human
Zheqian Chen, Rongqin Yang, Bin Cao, Zhou Zhao, Deng Cai, Xiaofei He
Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question. Many existing approaches on MC task are suffering the inefficiency in some bottlenecks, such as insufficient lexical understanding, complex question-passage interaction, incorrect answer extraction and so on. In this paper, we address these problems from the viewpoint of how humans deal with reading tests in a scientific way. Specifically, we first propose a novel lexical gating mechanism to dynamically combine the words and characters representations. We then guide the machines to read in an interactive way with attention mechanism and memory network. Finally we add a checking layer to refine the answer for insurance. The extensive experiments on two popular datasets SQuAD and TriviaQA show that our method exceeds considerable performance than most state-of-the-art solutions at the time of submission.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | SQuAD1.1 | smarnet (single model) | EM | 71.415 | #149 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | smarnet (single model) | F1 | 80.160 | #149 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | Smarnet | EM | 71.362 | #33 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | Smarnet | F1 | 80.183 | #33 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.
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