# BERT with History Answer Embedding for Conversational Question Answering

## TL;DR

This paper introduces a simple yet effective method called history answer embedding to incorporate conversation history into BERT-based models for conversational question answering, improving multi-turn interaction handling.

## Contribution

It proposes a novel history answer embedding technique that seamlessly integrates conversation history into BERT for ConvQA, simplifying and enhancing history modeling.

## Key findings

- Effective integration of conversation history improves answer accuracy.
- Analysis of history turn impact offers new insights into ConvQA modeling.
- Proposed method outperforms existing approaches on benchmark datasets.

## Abstract

Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question. Existing methods either prepend history turns to the current question or use complicated attention mechanisms to model the history. We propose a conceptually simple yet highly effective approach referred to as history answer embedding. It enables seamless integration of conversation history into a conversational question answering (ConvQA) model built on BERT (Bidirectional Encoder Representations from Transformers). We first explain our view that ConvQA is a simplified but concrete setting of conversational search, and then we provide a general framework to solve ConvQA. We further demonstrate the effectiveness of our approach under this framework. Finally, we analyze the impact of different numbers of history turns under different settings to provide new insights into conversation history modeling in ConvQA.