# VOGUE: Answer Verbalization through Multi-Task Learning

arXiv: https://arxiv.org/abs/2106.13316 · HTML: https://mlsanity.com/paper/2106.13316/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges](https://mlsanity.com/paper/2007.13069/) (2020)
- [Towards Fully 8-bit Integer Inference for the Transformer Model](https://mlsanity.com/paper/2009.08034/) (2020)
- [RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network](https://mlsanity.com/paper/2009.08694/) (2020)
- [Structural Information Preserving for Graph-to-Text Generation](https://mlsanity.com/paper/2102.06749/) (2021)
- [Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks](https://mlsanity.com/paper/2104.01569/) (2021)
- [Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models](https://mlsanity.com/paper/2008.05190/) (2020)
- [ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation](https://mlsanity.com/paper/2103.07771/) (2021)
- [MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities](https://mlsanity.com/paper/2008.06376/) (2020)
- [Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2103.07766/) (2021)
- [VANiLLa : Verbalized Answers in Natural Language at Large Scale](https://mlsanity.com/paper/2105.11407/) (2021)
- [Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning](https://mlsanity.com/paper/2001.10468/) (2020)

## Built on by (most cited first, 3 of 3)

- [Learning to Teach Fairness-aware Deep Multi-task Learning](https://mlsanity.com/paper/2206.08403/) (2022)
- [Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2210.04373/) (2022)
- [An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs](https://mlsanity.com/paper/2208.06734/) (2022)

## Abstract (arXiv)

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.