# Technical report on Conversational Question Answering

Source: [Technical report on Conversational Question Answering](<https://www.sota2.com/research/paper/68fa66601e209131cea0ff30>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:48:29.028Z

Authors: Ying Ju, Fubang Zhao, Shijie Chen, Bowen Zheng, Xuefeng Yang, Yunfeng Liu

Published: 2019-09-24T09:26:24.000Z

## Abstract

Conversational Question Answering is a challenging task since it requires understanding of conversational history. In this project, we propose a new system RoBERTa + AT +KD, which involves rationale tagging multi-task, adversarial training, knowledge distillation and a linguistic post-process strategy. Our single model achieves 90.4(F1) on the CoQA test set without data augmentation, outperforming the current state-of-the-art single model by 2.6% F1.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.10772>)

[PDF](<https://arxiv.org/pdf/1909.10772.pdf>)