activity
20192022
most citedA Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2022

Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions

Yuya Asano, Diane Litman, Mingzhi Yu +4

Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been sho…

cs.CL20211 cited

A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations

Mingzhi Yu, Diane Litman, Shuang Ma +1

Linguistic entrainment is a phenomenon where people tend to mimic each other in conversation. The core instrument to quantify entrainment is a linguistic similarity measure between…

cs.CL2021

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

Mingzhi Yu, Diane Litman

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 selectio…

cs.CL2019

Identifying Personality Traits Using Overlap Dynamics in Multiparty Dialogue

Mingzhi Yu, Emer Gilmartin, Diane Litman

Research on human spoken language has shown that speech plays an important role in identifying speaker personality traits. In this work, we propose an approach for identifying spea…

cs.CL2019

Investigating the Relationship between Multi-Party Linguistic Entrainment, Team Characteristics, and the Perception of Team Social Outcomes

Mingzhi Yu, Diane Litman, Susannah Paletz

Multi-party linguistic entrainment refers to the phenomenon that speakers tend to speak more similarly during conversation. We first developed new measures of multi-party entrainme…