@article{Yu_Litman_2021, title={Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT}, volume={34}, url={https://journals.flvc.org/FLAIRS/article/view/128491}, DOI={10.32473/flairs.v34i1.128491}, abstractNote={<p>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.</p>}, journal={The International FLAIRS Conference Proceedings}, author={Yu, Mingzhi and Litman, Diane}, year={2021}, month={Apr.} }