# Fluent Response Generation for Conversational Question Answering

arXiv: https://arxiv.org/abs/2005.10464 · HTML: https://mlsanity.com/paper/2005.10464/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 15 of 15)

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- [Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://mlsanity.com/paper/2310.13505/) (2023)
- [Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation](https://mlsanity.com/paper/2110.07150/) (2021)
- [Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering](https://mlsanity.com/paper/2103.06500/) (2021)
- [Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components](https://mlsanity.com/paper/2407.00997/) (2024)
- [Using Synthetic Data for Conversational Response Generation in Low-resource Settings](https://mlsanity.com/paper/2204.02653/) (2022)
- [An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs](https://mlsanity.com/paper/2208.06734/) (2022)
- [Natural Response Generation for Chinese Reading Comprehension](https://mlsanity.com/paper/2302.08817/) (2023)
- [Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2211.03648/) (2022)
- [A Joint-Reasoning based Disease Q&A System](https://mlsanity.com/paper/2401.03181/) (2024)

## Abstract (arXiv)

Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language generation (NLG) aspect of high-quality conversational agents. In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. From a technical perspective, we use data augmentation to generate training data for an end-to-end system. Specifically, we develop Syntactic Transformations (STs) to produce question-specific candidate answer responses and rank them using a BERT-based classifier (Devlin et al., 2019). Human evaluation on SQuAD 2.0 data (Rajpurkar et al., 2018) demonstrate that the proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. We further show our model's scalability by conducting tests on the CoQA dataset. The code and data are available at https://github.com/abaheti95/QADialogSystem.