Multi Document Reading Comprehension
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 62 references that are in the map. First posted on arXiv 5 Jan 2022.
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32 of 62 references.
References · 32
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
- 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
- 2015VQA: Visual Question Answeringcited by 2,489
- 2018Deep contextualized word representationscited by 2,285
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
- 2015Teaching Machines to Read and Comprehendcited by 1,039
- 2016The LAMBADA dataset: Word prediction requiring a broad discourse contextcited by 960
- 2015Pointer Networkscited by 882
- 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
20 more
- 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
- 2018CoQA: A Conversational Question Answering Challengecited by 674
- 2016NewsQA: A Machine Comprehension Datasetcited by 330
- 2016Hierarchical Question-Image Co-Attention for Visual Question Answeringcited by 278
- 2017SearchQA: A New Q&A Dataset Augmented with Context from a Search Enginecited by 221
- 2015The Goldilocks Principle: Reading Children's Books with Explicit Memory Representationscited by 108
- 2016A Thorough Examination of the CNN/Daily Mail Reading Comprehension Taskcited by 71
- 2016Machine Comprehension Using Match-LSTM and Answer Pointercited by 47
- 2019KBQA: Learning Question Answering over QA Corpora and Knowledge Basescited by 44
- 2018CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehensioncited by 34
- 2016WikiReading: A Novel Large-scale Language Understanding Task over Wikipediacited by 26
- 2016Who did What: A Large-Scale Person-Centered Cloze Datasetcited by 24
- 2016Hierarchical Question Answering for Long Documentscited by 21
- 2020Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Surveycited by 21
- 2016Text Understanding with the Attention Sum Reader Networkcited by 18
- 2018Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verificationcited by 17
- 2019Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehensioncited by 15
- 2017DCN+: Mixed Objective and Deep Residual Coattention for Question Answeringcited by 12
- 2018A Deep Cascade Model for Multi-Document Reading Comprehensioncited by 11
- 2016Iterative Alternating Neural Attention for Machine Readingcited by 6
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