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

arXiv: https://arxiv.org/abs/2208.06734 · HTML: https://mlsanity.com/paper/2208.06734/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [Question Rewriting for Conversational Question Answering](https://mlsanity.com/paper/2004.14652/) (2020)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph](https://mlsanity.com/paper/1801.10314/) (2018)
- [A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges](https://mlsanity.com/paper/2007.13069/) (2020)
- [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019)
- [Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks](https://mlsanity.com/paper/2104.01569/) (2021)
- [Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2105.04850/) (2021)
- [Fluent Response Generation for Conversational Question Answering](https://mlsanity.com/paper/2005.10464/) (2020)
- [ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation](https://mlsanity.com/paper/2103.07771/) (2021)
- [Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2103.07766/) (2021)
- [Sound Natural: Content Rephrasing in Dialog Systems](https://mlsanity.com/paper/2011.01993/) (2020)
- [VANiLLa : Verbalized Answers in Natural Language at Large Scale](https://mlsanity.com/paper/2105.11407/) (2021)
- [Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering](https://mlsanity.com/paper/2103.06500/) (2021)
- [VOGUE: Answer Verbalization through Multi-Task Learning](https://mlsanity.com/paper/2106.13316/) (2021)

## Built on by (most cited first, 1 of 1)

- [Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2210.04373/) (2022)

## Abstract (arXiv)

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.