An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs
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Abstract (arXiv)
We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun conversational question answering (ConvQA). However, in a real-world scenario (e.g., voice assistants such as Siri, Alexa, and Google Assistant), users prefer verbalized answers. This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with multiple paraphrased verbalized answers. We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness. We additionally perform an error analysis that details the rates of models' mispredictions in specified categories. Our proposed dataset extended with answer verbalization is publicly available with detailed documentation on its usage for wider utility.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 13 Aug 2022.
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22 of 34 references.
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- 2019Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansioncited by 42
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