Classifying Dialogue Acts in One-on-One Live Chats

We explore the task of automatically classifying dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service. In particular, we investigate the effectiveness of various features and machine learners for this task. While a simple bag-of-words approach provides a solid baseline, we find that adding information from dialogue structure and inter-utterance dependency provides some increase in performance; learners that account for sequential dependencies (CRFs) show the best performance. We report our results from testing using a corpus of chat dialogues derived from online shopping customer-feedback data.

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Su Nam Kim, Lawrence Cavedon, Timothy Baldwin
• Keywords: Dialogues, EMNLP 2010, Machine Learners, Natural Language Processing, Popular Means