Discovering Conversational Dependencies between Messages in Dialogs
This addresses the need for better understanding message relationships in customer service chats, but it is incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of inferring conversational dependencies between messages in one-on-one online chat, specifically in customer service contexts, and found that their proposed probabilistic classifier outperformed heuristic baselines.
We investigate the task of inferring conversational dependencies between messages in one-on-one online chat, which has become one of the most popular forms of customer service. We propose a novel probabilistic classifier that leverages conversational, lexical and semantic information. The approach is evaluated empirically on a set of customer service chat logs from a Chinese e-commerce website. It outperforms heuristic baselines.