# Reinforcement Learning for Conversational Question Answering over Knowledge Graph

Source: [Reinforcement Learning for Conversational Question Answering over Knowledge Graph](<https://www.sota2.com/research/paper/68fa71dc1e209131cea53683>)

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

Data updated: 2025-10-28T13:57:03.846Z

Authors: Mi Wu

Published: 2024-01-16T16:09:56.000Z

## Abstract

Conversational question answering (ConvQA) over law knowledge bases (KBs) involves answering multi-turn natural language questions about law and hope to find answers in the law knowledge base. Despite many methods have been proposed. Existing law knowledge base ConvQA model assume that the input question is clear and can perfectly reflect user's intention. However, in real world, the input questions are noisy and inexplict. This makes the model hard to find the correct answer in the law knowledge bases. In this paper, we try to use reinforcement learning to solve this problem. The reinforcement learning agent can automatically learn how to find the answer based on the input question and the conversation history, even when the input question is inexplicit. We test the proposed method on several real world datasets and the results show the effectivenss of the proposed model.

## 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/2401.08460>)

[PDF](<https://arxiv.org/pdf/2401.08460.pdf>)