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We introduce a new dataset for conversational question answering overKnowledge Graphs (KGs) with verbalized answers. Question answering over KGs iscurrently focused on answer generation for single-turn questions (KGQA) ormultiple-tun conversational question answering (ConvQA). However, in areal-world scenario (e.g., voice assistants such as Siri, Alexa, and GoogleAssistant), users prefer verbalized answers. This paper contributes to thestate-of-the-art by extending an existing ConvQA dataset with multipleparaphrased verbalized answers. We perform experiments with fivesequence-to-sequence models on generating answer responses while maintaininggrammatical correctness. We additionally perform an error analysis that detailsthe rates of models' mispredictions in specified categories. Our proposeddataset extended with answer verbalization is publicly available with detaileddocumentation on its usage for wider utility.

An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs