Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a powerful pre-trained Language Model as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationship among the triplet of passage, question and answers. While the latest pre-trained Language Models have shown powerful enough even without the support from a matching network, and the latest matching network has been complicated enough, we thus propose a novel going-back-to-the-basic solution which straightforwardly models the MRC relationship as attention mechanism inside network . The proposed DUal Multi-head Co-Attention (DUMA ) has been shown simple but effective and is capable of generally promoting pre-trained Language Models . Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of strong Language Models, DUMA may still boost the model to reach new state-of-the-art performance.
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a powerful Pre-trained Language Model (PrLM) as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationships among the triplet of passage, question and answers. While the newer and more powerful PrLMs have shown their mightiness even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by human's transposition thinking process solving the multi-choice MRC problem: respectively considering each other's focus from the standpoint of passage and question . The proposed DUMA has been shown effective and is capable of generally promoting PrLMs . Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of powerful PrLMs, DUMA can still boost the model to reach new state-of-the-art performance.
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pre-trained Language Model
Pre-trained Language Model (PrLM)
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relationship
relationships
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latest pre-trained Language Models have shown powerful enough
newer and more powerful PrLMs have shown their mightiness
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and the latest matching network has been complicated enough, we thus propose a novel going-back-to-the-basic solution which straightforwardly models the MRC relationship as attention mechanism inside network
we propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by human's transposition thinking process solving the multi-choice MRC problem: respectively considering each other's focus from the standpoint of passage and question
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DUal Multi-head Co-Attention (DUMA )
DUMA
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simple but
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pre-trained Language Models
PrLMs
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strong Language Models, DUMA may
powerful PrLMs, DUMA can