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        <title>Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder</title>
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            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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            <namePart type="given">Rajeev</namePart>
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    <abstract>In text matching applications, coattentions have proved to be highly effective attention mechanisms. Coattention enables the learning to attend based on computing word level affinity scores between two texts. In this paper, we propose two improvements to coattention mechanism in the context of passage ranking (re-ranking). First, we extend the coattention mechanism by applying it across all word n-grams of query and passage. We show that these word n-gram coattentions can capture local context in query and passage to better judge the relevance between them. Second, we further improve the model performance by proposing a query based attention pooling on passage encodings. We evaluate these two methods on MSMARCO passage re-ranking task. The experiment results shows that these two methods resulted in a relative increase of 8.04% in Mean Reciprocal Rank @10 (MRR@10) compared to the naive coattention mechanism. At the time of writing this paper, our methods are the best non transformer model on MS MARCO passage re-ranking task and are competitive to BERT base while only having less than 10% of the parameters.</abstract>
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%0 Conference Proceedings
%T Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder
%A Alaparthi, Chaitanya
%A Shrivastava, Manish
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F alaparthi-shrivastava-2020-improving
%X In text matching applications, coattentions have proved to be highly effective attention mechanisms. Coattention enables the learning to attend based on computing word level affinity scores between two texts. In this paper, we propose two improvements to coattention mechanism in the context of passage ranking (re-ranking). First, we extend the coattention mechanism by applying it across all word n-grams of query and passage. We show that these word n-gram coattentions can capture local context in query and passage to better judge the relevance between them. Second, we further improve the model performance by proposing a query based attention pooling on passage encodings. We evaluate these two methods on MSMARCO passage re-ranking task. The experiment results shows that these two methods resulted in a relative increase of 8.04% in Mean Reciprocal Rank @10 (MRR@10) compared to the naive coattention mechanism. At the time of writing this paper, our methods are the best non transformer model on MS MARCO passage re-ranking task and are competitive to BERT base while only having less than 10% of the parameters.
%U https://aclanthology.org/2020.icon-main.21
%P 161-169

Markdown (Informal)

[Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder](https://aclanthology.org/2020.icon-main.21) (Alaparthi & Shrivastava, ICON 2020)

• Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder (Alaparthi & Shrivastava, ICON 2020)

ACL

• Chaitanya Alaparthi and Manish Shrivastava. 2020. Improving Passage Re-Ranking with Word N-Gram Aware Coattention Encoder. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 161–169, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).