# Deep Understanding based Multi-Document Machine Reading Comprehension

arXiv: https://arxiv.org/abs/2204.03494 · HTML: https://mlsanity.com/paper/2204.03494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 40 of its 57 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 30 of 40)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://mlsanity.com/paper/1804.09541/) (2018)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Simple and Effective Multi-Paragraph Reading Comprehension](https://mlsanity.com/paper/1710.10723/) (2017)
- [Semantics-aware BERT for Language Understanding](https://mlsanity.com/paper/1909.02209/) (2019)
- [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://mlsanity.com/paper/1711.05073/) (2017)
- [Retrospective Reader for Machine Reading Comprehension](https://mlsanity.com/paper/2001.09694/) (2020)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [SG-Net: Syntax-Guided Machine Reading Comprehension](https://mlsanity.com/paper/1908.05147/) (2019)
- [Machine Comprehension Using Match-LSTM and Answer Pointer](https://mlsanity.com/paper/1608.07905/) (2016)
- [Attention-over-Attention Neural Networks for Reading Comprehension](https://mlsanity.com/paper/1607.04423/) (2016)
- [Read + Verify: Machine Reading Comprehension with Unanswerable Questions](https://mlsanity.com/paper/1808.05759/) (2018)
- [Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension](https://mlsanity.com/paper/2005.08056/) (2020)
- [Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering](https://mlsanity.com/paper/1811.11934/) (2018)
- [Stochastic Answer Networks for Machine Reading Comprehension](https://mlsanity.com/paper/1712.03556/) (2017)
- [Self-Supervised Test-Time Learning for Reading Comprehension](https://mlsanity.com/paper/2103.11263/) (2021)
- [Improving Machine Reading Comprehension with General Reading Strategies](https://mlsanity.com/paper/1810.13441/) (2018)
- [Bridging Information-Seeking Human Gaze and Machine Reading Comprehension](https://mlsanity.com/paper/2009.14780/) (2020)
- [Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification](https://mlsanity.com/paper/1805.02220/) (2018)
- [Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension](https://mlsanity.com/paper/2005.05806/) (2020)

## Abstract (arXiv)

Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings. First, to understand the semantic meaning of words in the input question and documents from the perspective of each other. Second, to understand the supporting cues for a correct answer from the perspective of intra-document and inter-documents. Ignoring these two kinds of important understandings would make the models oversee some important information that may be helpful for inding correct answers. To overcome this deiciency, we propose a deep understanding based model for multi-document machine reading comprehension. It has three cascaded deep understanding modules which are designed to understand the accurate semantic meaning of words, the interactions between the input question and documents, and the supporting cues for the correct answer. We evaluate our model on two large scale benchmark datasets, namely TriviaQA Web and DuReader. Extensive experiments show that our model achieves state-of-the-art results on both datasets.