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Conversational search systems, such as Google Assistant and MicrosoftCortana, provide a new search paradigm where users are allowed, via naturallanguage dialogues, to communicate with search systems. Evaluating such systemsis very challenging since search results are presented in the format of naturallanguage sentences. Given the unlimited number of possible responses,collecting relevance assessments for all the possible responses is infeasible.In this paper, we propose POSSCORE, a simple yet effective automatic evaluationmethod for conversational search. The proposed embedding-based metric takes theinfluence of part of speech (POS) of the terms in the response into account. Tothe best knowledge, our work is the first to systematically demonstrate theimportance of incorporating syntactic information, such as POS labels, forconversational search evaluation. Experimental results demonstrate that ourmetrics can correlate with human preference, achieving significant improvementsover state-of-the-art baseline metrics.

POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling