Segmenting Conversations by Topic, Initiative, and Style
Information Retrieval Techniques for Speech Applications [this book is based on the workshop “Information Retrieval Techniques for Speech Applications”, held as part of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in New Orleans, USA, in September 2001].
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Surface-marker-based dialog modelling: A progress report on the MAREDI project
Natural Language Engineering
DiaSumm: flexible summarization of spontaneous dialogues in unrestricted domains
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Latent semantic analysis for dialogue act classification
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
FLSA: extending latent semantic analysis with features for dialogue act classification
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Combining lexical, syntactic and prosodic cues for improved online dialog act tagging
Computer Speech and Language
Automatic annotation of context and speech acts for dialogue corpora
Natural Language Engineering
Investigating the portability of corpus-derived cue phrases for dialogue act classification
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Tagging and linking web forum posts
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Text based dialog act classification for multiparty meetings
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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We present an incremental lattice generation approach to speech act detection for spontaneous and overlapping speech in telephone conversations (CallHome Spanish). At each stage of the process it is therefore possible to use different models after the initial HMM models have generated a reasonable set of hypothesis. These lattices can be processed further by more complex models. This study shows how neural networks can be used very effectively in the classification of speech acts. We find that speech acts can be classified better using the neural net based approach than using the more classical ngram backoff model approach. The best resulting neural network operates only on unigrams and the integration of the ngram backoff model as a prior to the model reduces the performance of the model. The neural network can therefore more likely be robust against errors from an LVCSR system and can potentially be trained from a smaller database.