Discovering Conversational Dependencies between Messages in Dialogs

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.

cs.CL Computer Science

View graph Source

Paper

References (6)

01Discourse parsing for multi-party chat dialoguesStergos D. Afantenos, Eric Kow, Nicholas Asher et al.2015 · Conference on Empirical Methods in Natural Language Processing · 84 citations

02A Neural Network Approach to Context-Sensitive Generation of Conversational ResponsesAlessandro Sordoni, Michel Galley, Michael Auli et al.2015 · North American Chapter of the Association for Computational Linguistics · 913 citations In Library

03Disentangling ChatMicha Elsner, Eugene Charniak2010 · International Conference on Computational Logic · 119 citations

04Unsupervised Modeling of Twitter ConversationsAlan Ritter, Colin Cherry, W. Dolan2010 · North American Chapter of the Association for Computational Linguistics · 503 citations

05Thread detection in dynamic text message streamsDou Shen, Qiang Yang, Jian-Tao Sun et al.2006 · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 131 citations

06and Charniak2010 · E.

Similar papers

Toward intelligent conversational systems: chatbots with behavior-aware AI modelEmanuele Fiocco, Irene Ariante, Vittorio Cesarotti2026 · Cineca Institutional Research Information System (Tor Vergata University) In Library

Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog SystemsNhat Le, A. B. Siddique, Fuad Jamour et al.2021 · International Journal of Semantic Computing In Library

Predictable and Adaptive Goal-oriented Dialog Policy GenerationNhat Le, A. B. Siddique, Fuad Jamour et al.2021 In Library

Detecting Context Dependent Messages in a Conversational EnvironmentChaozhuo Li, Yuehua Wu, Wei Wu et al.2016 · International Conference on Computational Linguistics · 13 citations In Library

Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language ModelsHao Yang, Hang Li, Wenbiao Ding et al.2021 · arXiv (Cornell University) · 2 citations In Library

A Pilot Study on Dialogue-Level Dependency Parsing for ChineseGongyao Jiang, Shuang Liu, Meishan Zhang et al.2023 In Library

A Pilot Study on Dialogue-Level Dependency Parsing for ChineseGongyao Jiang, Shuang Liu, Meishan Zhang et al.2023 · arXiv (Cornell University) · 2 citations In Library

Improve Chit-Chat and QA Sentence Classification in User Messages of Dialogue System using Dialogue Act Embedding.Chi Hsiang Chao, Xi Jie Hou, Yu Ching Chiu2021 · International Conference on Computational Linguistics In Library

Detection of Hidden Feature Requests from Massive Chat Messages via Deep Siamese NetworkLin Shi, Mingzhe Xing, Mingyang Li et al.2020 · International Conference on Software Engineering · 38 citations In Library

LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live StreamingJingsheng Gao, Yixin Lian, Ziyi Zhou et al.2023 In Library

Source of record

arXiv

44B

Welcome.

This panel sticks with you. Pick anything below and it opens right beside it, so you can dig through 60,000-plus records without ever losing your spot here.

Travel 44B

LibraryPapers, policy, standards, statute LabsEvery organization building AI ModelsIntelligence, price, and speed BenchmarksThe evaluation catalog IncidentsAI failures and harms SearchOne field across everything DashboardThe state of AI accountability in NY 44B RegistryThe Art. 44-B compliance portal