# Prompt Guided Copy Mechanism for Conversational Question Answering

arXiv: https://arxiv.org/abs/2308.03422 · HTML: https://mlsanity.com/paper/2308.03422/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [BERT with History Answer Embedding for Conversational Question Answering](https://mlsanity.com/paper/1905.05412/) (2019)
- [Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data](https://mlsanity.com/paper/2203.08773/) (2022)
- [PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation](https://mlsanity.com/paper/2004.07159/) (2020)
- [Training Deeper Neural Machine Translation Models with Transparent Attention](https://mlsanity.com/paper/1808.07561/) (2018)
- [FlowQA: Grasping Flow in History for Conversational Machine Comprehension](https://mlsanity.com/paper/1810.06683/) (2018)
- [Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering](https://mlsanity.com/paper/2106.11575/) (2021)
- [Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory](https://mlsanity.com/paper/2004.02214/) (2020)
- [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://mlsanity.com/paper/2205.04040/) (2022)
- [Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering](https://mlsanity.com/paper/2103.06500/) (2021)

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

- [A Survey of the State-of-the-Art in Conversational Question Answering Systems](https://mlsanity.com/paper/2509.05716/) (2025)

## Abstract (arXiv)

Conversational Question Answering (CQA) is a challenging task that aims to generate natural answers for conversational flow questions. In this paper, we propose a pluggable approach for extractive methods that introduces a novel prompt-guided copy mechanism to improve the fluency and appropriateness of the extracted answers. Our approach uses prompts to link questions to answers and employs attention to guide the copy mechanism to verify the naturalness of extracted answers, making necessary edits to ensure that the answers are fluent and appropriate. The three prompts, including a question-rationale relationship prompt, a question description prompt, and a conversation history prompt, enhance the copy mechanism's performance. Our experiments demonstrate that this approach effectively promotes the generation of natural answers and achieves good results in the CoQA challenge.