MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. |
| Approach: | They propose a multi-perspective convolutional cube model that integrates 1D and 2D convolutions with recurrent neural networks (RNN) to understand context from different perspectives. |
| Outcome: | The proposed model is based on the Conversational Question Answering (CoQA) dataset and achieves state-of-the-art results. |
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| Challenge: | Conversational machine comprehension (CMC) is a research track in conversational AI. |
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Nuo Chen, Hongguang Li, Junqing He, Yinan Bao, Xinshi Lin, Qi Yang, Jianfeng Liu, Ruyi Gan, Jiaxing Zhang, Baoyuan Wang, Jia Li
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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. |
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| Challenge: | Existing datasets focused on single-perspective question-answer pairs overlooking inherent variation in comprehension levels among different readers. |
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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. |
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| Challenge: | DuoRC contains 186,089 unique question-answer pairs created from 7680 movie plots . |
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Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
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| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
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ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods require three steps to understand text, but span extraction and question rephrasing steps are not fully exploited. |
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