  In recent years, there have been significant developments in Question
Answering over Knowledge Graphs (KGQA). Despite all the notable advancements,
current KGQA systems only focus on answer generation techniques and not on
answer verbalization. However, in real-world scenarios (e.g., voice assistants
such as Alexa, Siri, etc.), users prefer verbalized answers instead of a
generated response. This paper addresses the task of answer verbalization for
(complex) question answering over knowledge graphs. In this context, we propose
a multi-task-based answer verbalization framework: VOGUE (Verbalization thrOuGh
mUlti-task lEarning). The VOGUE framework attempts to generate a verbalized
answer using a hybrid approach through a multi-task learning paradigm. Our
framework can generate results based on using questions and queries as inputs
concurrently. VOGUE comprises four modules that are trained simultaneously
through multi-task learning. We evaluate our framework on existing datasets for
answer verbalization, and it outperforms all current baselines on both BLEU and
METEOR scores.
