Recently, empathetic dialogue systems have received significant attention.
While some researchers have noted limitations, e.g., that these systems tend to generate generic utterances, no study has systematically verified these issues. We survey 21 systems, asking what progress has been made on the task. We observe multiple limitations of current evaluation procedures. Most critically, studies tend to rely on a single non-reproducible empathy score, which inadequately reflects the multidimensional nature of empathy. To better understand the differences between systems, we comprehensively analyze each system with automated methods that are grounded in a variety of aspects of empathy. We find that recent systems lack three important aspects of empathy: specificity, reflection levels, and diversity. Based on our results, we discuss problematic behaviors that may have gone undetected in prior evaluations, and offer guidance for developing future systems.\footnote{Our experiments can be found at [Anonymous URL]}

A Comparative Multidimensional Analysis of Empathetic Systems

Understanding empathy in text dialogue data is a difficult, yet critical, skill for effective human-machine interaction. In this work, we ask whether systems are making meaningful progress on this challenge. We consider a simple model that checks if an input utterance is similar to a small set of empathetic examples. Crucially, the model does not look at what the utterance is a response to, i.e., the dialogue context. This model performs comparably to other work on standard benchmarks and even outperforms state-of-the-art models for empathetic rationale extraction by 16.7 points on T-F1 and 4.3 on IOU-F1. This indicates that current systems rely on the surface form of the response, rather than whether it is suitable in context. To confirm this, we create examples with dialogue contexts that change the interpretation of the response and show that current systems continue to label utterances as empathetic. We discuss the implications of our findings, including improvements for empathetic benchmarks and how our model can be an informative baseline.

Empathy Identification Systems are not Accurately Accounting for Context

Dialogue and Interactive Systems

technical paper

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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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A Comparative Multidimensional Analysis of Empathetic Systems

Empathy Identification Systems are not Accurately Accounting for Context

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