Hierarchical Dependence-aware Evaluation Measures for Conversational Search

Conversational agents are drawing a lot of attention in the information retrieval (IR) community also thanks to the advancements in language understanding enabled by large contextualized language models. IR researchers have long ago recognized the importance o fa sound evaluation of new approaches. Yet, the development of evaluation techniques for conversational search is still an underlooked problem. Currently, most evaluation approaches rely on procedures directly drawn from ad-hoc search evaluation, treating utterances in a conversation as independent events, as if they were just separate topics, instead of accounting for the conversation context. We overcome this issue by proposing a framework for defining evaluation measures that are aware of the conversation context and the utterance semantic dependencies. In particular, we model the conversations as Direct Acyclic Graphs (DAG), where self-explanatory utterances are root nodes, while anaphoric utterances are linked to sentences that contain their missing semantic information. Then,we propose a family of hierarchical dependence-aware aggregations of the evaluation metrics driven by the conversational graph. In our experiments, we show that utterances from the same conversation are 20% more correlated than utterances from different conversations. Thanks to the proposed framework, we are able to include such correlation in our aggregations, and be more accurate when determining which pairs of conversational systems are deemed significantly different.