An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs

TL;DR

This paper introduces a new dataset for conversational question answering over Knowledge Graphs that includes multiple paraphrased verbalized answers, aiming to improve answer generation for real-world voice assistant scenarios.

Contribution

It extends an existing conversational QA dataset with verbalized answers and provides baseline experiments with sequence-to-sequence models for answer generation.

Findings

Models can generate grammatically correct verbalized answers

Error analysis reveals common misprediction categories

Dataset is publicly available for further research

Abstract

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.