most citedMDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation

8 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2022

PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment

Chen Zhang, Luis Fernando D'Haro, Qiquan Zhang +2

Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure…

cs.CL2022

FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluation

Chen Zhang, Luis Fernando D'Haro, Qiquan Zhang +2

Recent model-based reference-free metrics for open-domain dialogue evaluation exhibit promising correlations with human judgment. However, they either perform turn-level evaluation…

cs.CL2022★ 8 cited

MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation

Chen Zhang, Luis Fernando D'Haro, Thomas Friedrichs +1

Chatbots are designed to carry out human-like conversations across different domains, such as general chit-chat, knowledge exchange, and persona-grounded conversations. To measure…

cs.CL2021★ 1 cited

Investigating the Impact of Pre-trained Language Models on Dialog Evaluation

Chen Zhang, Luis Fernando D'Haro, Yiming Chen +2

Recently, there is a surge of interest in applying pre-trained language models (Pr-LM) in automatic open-domain dialog evaluation. Pr-LMs offer a promising direction for addressing…

cs.CL2021★ 7 cited

DynaEval: Unifying Turn and Dialogue Level Evaluation

Chen Zhang, Yiming Chen, Luis Fernando D'Haro +4

A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are…