Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT

Mingzhi Yu; Diane Litman

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arXiv · 2105.13479

Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT

Mingzhi Yu·Diane Litman

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Abstract

Retrieval-based dialogue systems select the best response from many candidates. Although many state-of-the-art models have shown promising performance in dialogue response selection tasks, there is still quite a gap between R@1 and R@10 performance. To address this, we propose to leverage linguistic coordination (a phenomenon that individuals tend to develop similar linguistic behaviors in conversation) to rerank the N-best candidates produced by BERT, a state-of-the-art pre-trained language model. Our results show an improvement in R@1 compared to BERT baselines, demonstrating the utility of repairing machine-generated outputs by leveraging a linguistic theory.

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Mingzhi Yu, Diane Litman. 2021-05-27. Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT. https://arxiv.org/abs/2105.13479

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