VOGUE: Answer Verbalization Through Multi-Task Learning

• Published in: Machine Learning and Knowledge Discovery in Databases. Research Track Vol. 12977; pp. 563 - 579
• Main Authors: Kacupaj, Endri, Premnadh, Shyamnath, Singh, Kuldeep, Lehmann, Jens, Maleshkova, Maria
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Answer verbalization; Knowledge graphs; Multi-task learning; Natural language generation; Question answering
• ISBN: 3030865223; 9783030865221
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-86523-8_34

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.