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Stochastic Answer Networks for Machine Reading Comprehension

arXiv 1712.03556 ↗First posted 10 Dec 2017cs.CLcited by 22

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Abstract (arXiv)

We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2017.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 3; 2024: 3; 2025: 1.

22 papers.

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  1. 2021ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generationcited by 28
  2. 2021NoiseQA: Challenge Set Evaluation for User-Centric Question Answeringcited by 25
  3. 2021Do Question Answering Modeling Improvements Hold Across Benchmarks?cited by 20
  4. 2021Is Everything in Order? A Simple Way to Order Sentencescited by 15
  5. 2022JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehensioncited by 7
  6. 2022iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Modelscited by 3
  7. 2024GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?cited by 3
  8. 2021MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehensioncited by 2
  9. 2023A Comprehensive Empirical Evaluation of Existing Word Embedding Approachescited by 1
  10. 2024Engineering A Large Language Model From Scratchcited by 1
  11. 2024Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Surveycited by 1
  12. 2025Evaluating List Construction and Temporal Understanding capabilities of Large Language Modelscited by 1
10 more
  1. 2021Understanding in Artificial Intelligencecited by 0
  2. 2021Automatic Task Requirements Writing Evaluation via Machine Reading Comprehensioncited by 0
  3. 2022An Automated Question-Answering Framework Based on Evolution Algorithmcited by 0
  4. 2022Pruned Graph Neural Network for Short Story Orderingcited by 0
  5. 2022Deep Understanding based Multi-Document Machine Reading Comprehensioncited by 0
  6. 2022Good Intentions: Adaptive Parameter Management via Intent Signalingcited by 0
  7. 2022An Understanding-Oriented Robust Machine Reading Comprehension Modelcited by 0
  8. 2022Feature-augmented Machine Reading Comprehension with Auxiliary Taskscited by 0
  9. 2023PEvoLM: Protein Sequence Evolutionary Information Language Modelcited by 0
  10. 2023Perturbation-based Active Learning for Question Answeringcited by 0