# Machine Comprehension Using Match-LSTM and Answer Pointer

arXiv: https://arxiv.org/abs/1608.07905 · HTML: https://mlsanity.com/paper/1608.07905/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2016.

## Built on by (most cited first, 30 of 47)

- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Natural Language Video Localization: A Revisit in Span-based Question Answering Framework](https://mlsanity.com/paper/2102.13558/) (2021)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising](https://mlsanity.com/paper/2105.07911/) (2021)
- [Selfish Sparse RNN Training](https://mlsanity.com/paper/2101.09048/) (2021)
- [Improving Unsupervised Question Answering via Summarization-Informed Question Generation](https://mlsanity.com/paper/2109.07954/) (2021)
- [Rethinking Search: Making Domain Experts out of Dilettantes](https://mlsanity.com/paper/2105.02274/) (2021)
- [Revisiting the Negative Data of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.10158/) (2021)
- [A Fine-grained Interpretability Evaluation Benchmark for Neural NLP](https://mlsanity.com/paper/2205.11097/) (2022)
- [NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset](https://mlsanity.com/paper/2109.10604/) (2021)
- [Asking questions on handwritten document collections](https://mlsanity.com/paper/2110.00711/) (2021)
- [A Comparative Study of Transformer-Based Language Models on Extractive Question Answering](https://mlsanity.com/paper/2110.03142/) (2021)
- [Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph](https://mlsanity.com/paper/2111.10541/) (2021)
- [KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering](https://mlsanity.com/paper/2205.03071/) (2022)
- [Unsupervised Question Answering via Answer Diversifying](https://mlsanity.com/paper/2208.10813/) (2022)
- [The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models](https://mlsanity.com/paper/2302.00094/) (2023)
- [Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision](https://mlsanity.com/paper/2107.08582/) (2021)
- [Noisy Text Data: Achilles' Heel of popular transformer based NLP models](https://mlsanity.com/paper/2110.03353/) (2021)
- [Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset](https://mlsanity.com/paper/2204.09659/) (2022)
- [ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning](https://mlsanity.com/paper/2102.12828/) (2021)
- [MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension](https://mlsanity.com/paper/2103.04567/) (2021)
- [Improving Low-resource Reading Comprehension via Cross-lingual Transposition Rethinking](https://mlsanity.com/paper/2107.05002/) (2021)
- [Interactive Machine Comprehension with Dynamic Knowledge Graphs](https://mlsanity.com/paper/2109.00077/) (2021)
- [QAGAN: Adversarial Approach To Learning Domain Invariant Language Features](https://mlsanity.com/paper/2206.12388/) (2022)
- [Adaptive loose optimization for robust question answering](https://mlsanity.com/paper/2305.03971/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension](https://mlsanity.com/paper/2103.14443/) (2021)
- [Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction](https://mlsanity.com/paper/2112.03849/) (2021)
- [ECO v1: Towards Event-Centric Opinion Mining](https://mlsanity.com/paper/2203.12264/) (2022)
- [Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System](https://mlsanity.com/paper/2206.12101/) (2022)

## Abstract (arXiv)

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.