| Challenge: | Reading comprehension models are dominated by recurrent neural networks (RNNs) as documents become longer and questions become complex, sequential reading becomes a significant bottleneck. |
| Approach: | They propose a reading comprehension framework that uses document trees to model an agent that interleaves quick navigation with more expensive answer extraction. |
| Outcome: | The proposed model improves question answering performance compared to existing models and has a strong information-retrieval baseline. |
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Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
| Approach: | They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text. |
| Outcome: | The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement on the recently proposed and challenging RC dataset ‘NarrativeQA’. |
Does Structure Matter? Encoding Documents for Machine Reading Comprehension (2021.naacl-main)
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| Challenge: | Existing Transformer-based models for machine reading comprehension treat documents as flat sequences. |
| Approach: | They propose a Transformer-based method that reads a document as tree slices and jointly trains and consults the modules at inference time. |
| Outcome: | The proposed method outperforms several baseline approaches on two datasets from varied domains. |
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to machine reading comprehension treat documents at their hierarchical nature, ignoring their dependencies. |
| Approach: | They propose a machine reading comprehension benchmark with two-grained answers . they use graph attention networks to model documents at their hierarchical nature . |
| Outcome: | The proposed framework outperforms existing systems at long and short answer criteria. |
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller (D18-1)
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| Challenge: | Existing approaches to machine reading comprehension are limited in understanding, up to a few paragraphs, failing to comprehend lengthy documents. |
| Approach: | They propose a deep neural network architecture to handle a long-range dependency in RC tasks. |
| Outcome: | The proposed method outperforms existing methods especially for lengthy documents. |
Question Answering by Reasoning Across Documents with Graph Convolutional Networks (N19-1)
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| Challenge: | Recent research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. |
| Approach: | They propose a neural model which integrates and reasons relying on information spread within documents and across multiple documents. |
| Outcome: | The proposed model achieves state-of-the-art on a multi-document question answering dataset, WikiHop. |
Contextualized Word Representations for Reading Comprehension (N18-2)
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| Challenge: | Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. |
| Approach: | They propose to provide a standard neural network for reading a document and answering a question about its content. |
| Outcome: | The proposed model improves on the competitive SQuAD dataset by providing rich contextualized word representations and allowing it to choose between context-dependent and context-independent representations. |
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
| Approach: | They propose to let a model learn to chunk in a more flexible way via reinforcement learning. |
| Outcome: | The proposed model extracts a text span from document and query as answer . previous models can only take a fixed-length (e.g., 512) text as input . |
On Making Reading Comprehension More Comprehensive (D19-58)
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| Challenge: | Getting machines to "understand" text is a vast and long-standing problem, made more challenging by the fact that it is not even clear what it means to understand text. |
| Approach: | They propose a question-based approach to machine reading comprehension that uses a natural language question to test a system's comprehension of a passage of text. |
| Outcome: | The proposed questions have surface cues or other biases that allow a model to shortcut the intended reasoning process. |
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)
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| Challenge: | Experimental results on 3 popular datasets demonstrate the effectiveness of our approach. |
| Approach: | They propose a network to solve the inference problem by decomposing text into a series of attention-based reasoning steps. |
| Outcome: | The proposed network can be used to understand the meanings of given text to answer questions. |
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives (P19-1)
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Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
| Challenge: | Using a pointer-generator framework for reading/sampling over large documents, we propose a framework for learning over long narratives where documents easily span over thousands of tokens. |
| Approach: | They propose a curriculum learning (CL) based pointer-generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. |
| Outcome: | The proposed framework improves on the NarrativeQA reading comprehension benchmark and reaches state-of-the-art performance. |