Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r> .7,

The paper attempts to move away from traditional evaluation of open-domain dialog systems (i.e., judge response given its conversation history) and moves towards a more interactive one (i.e., human talking to a bot), which is likely an important step towards better evaluation. However, I do have several serious concerns about this work in its current form: (1) The authors contrast their work with existing evaluation for open-domain dialog evaluation, which they call "single-turn" evaluation. They point out that this type of evaluation prevents it from capturing "failure modes […] such as a lack of diversity in the responses, inability to track long-term aspects of the conversation". I think this is rather misleading and the term is "single-turn" is a misnomer. Most previous work has indeed evaluated each conversation by factorizing it into a sequence of independent turn-level judgments, but each of these judgments assesses the quality of the current turn T_n **given** a history of several previous turns …, T_n-k, … T_n-1.

This paper explores interesting directions, in particular 1) using interactive settings to evaluate a model rather than a single answer, and 2) combining different automated metrics in a weighted sums to approximate human evaluation (e.g., based on sentiment). Reviewers have raised crucial points, regarding gameability (so that using the metrics for training a model is tricky if not followed by a non-gameable evaluation), and lack of comparability between different self-play. It's indeed a much better evaluation setting if the system does not control both sides (e.g., models being matched to the same set of fixed models), so authors should definitely follow that direction. However, I expect this work would still be interesting to the dialog community: many of the diagnostic advantages of the model-talking-to-model setting remain, in practice, especially because the model is in fact not trained with the self-play objective, but that criterion is only used post hoc (so the system can't extensively exploit it during training). In practice, a lot of the problems of the generations of a given model already show up during self-play, and the reasonable worry raised by reviewers that the model could exploit the metric remains theoretical at the moment.

Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r .7,

Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r .7,