  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.
