# Multi Document Reading Comprehension

arXiv: https://arxiv.org/abs/2201.01706 · HTML: https://mlsanity.com/paper/2201.01706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 62 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [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)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations](https://mlsanity.com/paper/1511.02301/) (2015)
- [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task](https://mlsanity.com/paper/1606.02858/) (2016)
- [Machine Comprehension Using Match-LSTM and Answer Pointer](https://mlsanity.com/paper/1608.07905/) (2016)
- [KBQA: Learning Question Answering over QA Corpora and Knowledge Bases](https://mlsanity.com/paper/1903.02419/) (2019)
- [CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension](https://mlsanity.com/paper/1803.09720/) (2018)
- [WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia](https://mlsanity.com/paper/1608.03542/) (2016)
- [Who did What: A Large-Scale Person-Centered Cloze Dataset](https://mlsanity.com/paper/1608.05457/) (2016)
- [Hierarchical Question Answering for Long Documents](https://mlsanity.com/paper/1611.01839/) (2016)
- [Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey](https://mlsanity.com/paper/2002.06612/) (2020)
- [Text Understanding with the Attention Sum Reader Network](https://mlsanity.com/paper/1603.01547/) (2016)
- [Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification](https://mlsanity.com/paper/1805.02220/) (2018)
- [Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension](https://mlsanity.com/paper/1906.04618/) (2019)
- [DCN+: Mixed Objective and Deep Residual Coattention for Question Answering](https://mlsanity.com/paper/1711.00106/) (2017)

## Abstract (arXiv)

Reading Comprehension (RC) is a task of answering a question from a given passage or a set of passages. In the case of multiple passages, the task is to find the best possible answer to the question. Recent trials and experiments in the field of Natural Language Processing (NLP) have proved that machines can be provided with the ability to not only process the text in the passage and understand its meaning to answer the question from the passage, but also can surpass the Human Performance on many datasets such as Standford's Question Answering Dataset (SQuAD). This paper presents a study on Reading Comprehension and its evolution in Natural Language Processing over the past few decades. We shall also study how the task of Single Document Reading Comprehension acts as a building block for our Multi-Document Reading Comprehension System. In the latter half of the paper, we'll be studying about a recently proposed model for Multi-Document Reading Comprehension - RE3QA that is comprised of a Reader, Retriever, and a Re-ranker based network to fetch the best possible answer from a given set of passages.