# An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs

Source: [An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs](<https://www.sota2.com/research/paper/68fa6d3b1e209131cea389d6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:29:35.891Z

Authors: Endri Kacupaj, Kuldeep Singh, Maria Maleshkova, Jens Lehmann

Published: 2022-08-13T21:21:28.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2208.06734>)

[PDF](<https://arxiv.org/pdf/2208.06734.pdf>)