most citedDynaEval: Unifying Turn and Dialogue Level Evaluation

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

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cs.CL2022

Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework

Yiming Chen, Yan Zhang, Bin Wang +2

Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performanc…

cs.CL2022

Analyzing and Evaluating Faithfulness in Dialogue Summarization

Bin Wang, Chen Zhang, Yan Zhang +2

Dialogue summarization is abstractive in nature, making it suffer from factual errors. The factual correctness of summaries has the highest priority before practical applications.…

cs.CL20211 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

Revisiting Self-Training for Few-Shot Learning of Language Model

Yiming Chen, Yan Zhang, Chen Zhang +3

As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In…

cs.CL20217 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…