tailieunhanh - Báo cáo khoa học: "Detection of Agreement and Disagreement in Broadcast Conversations"

We present Conditional Random Fields based approaches for detecting agreement/disagreement between speakers in English broadcast conversation shows. We develop annotation approaches for a variety of linguistic phenomena. Various lexical, structural, durational, and prosodic features are explored. | Detection of Agreement and Disagreement in Broadcast Conversations Wen Wang Sibel Yama Kristin Precoda1 Colleen Richey1 Geoffrey Raymond3 SRI International 333 Ravenswood Avenue Menlo Park CA 94025 USA 2IBM T. J. Watson Research Center 218 Yorktown Heights NY 10598 USA 3University of California Santa Barbara CA USA wwang precoda colleen @ syaman@ graymond@ Abstract We present Conditional Random Fields based approaches for detecting agree-ment disagreement between speakers in English broadcast conversation shows. We develop annotation approaches for a variety of linguistic phenomena. Various lexical structural durational and prosodic features are explored. We compare the performance when using features extracted from automatically generated annotations against that when using human annotations. We investigate the efficacy of adding prosodic features on top of lexical structural and durational features. Since the training data is highly imbalanced we explore two sampling approaches random downsampling and ensemble downsampling. Overall our approach achieves precision recall F1 for agreement detection and precision recall and F1 for disagreement detection on the English broadcast conversation data. 1 Introduction In this work we present models for detecting agreement disagreement denoted dis agreement between speakers in English broadcast conversation shows. The Broadcast Conversation BC genre differs from the Broadcast News BN genre in that it is more interactive and spontaneous referring to free speech in news-style TV and radio programs and consisting of talk shows interviews call-in programs live reports and round-tables. Previous This work was performed while the author was at ICSI. 374 work on detecting dis agreements has been focused on meeting data. Hillard et al. 2003 Galley et al. 2004 Hahn et al. 2006 used spurt-level agreement annotations from the ICSI meeting corpus Janin et al. .