Sentence Unit Detection in Conversational Dialogue Elizabeth Lingg, Tejaswi Tennetti, Anand Madhavan it has a lot of garlic in it too does n't it i it.

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Presentation transcript:

Sentence Unit Detection in Conversational Dialogue Elizabeth Lingg, Tejaswi Tennetti, Anand Madhavan it has a lot of garlic in it too does n't it i it does Speaker B Speaker A Prosodic features Sentence Units

Dataset used LDC2009T01 English CTS Treebank with Structural metadata LDC2009T01 English CTS Treebank with Structural metadata Highlights Fisher and Switchboard audio clips Words annotated with POS tags Sentence units labeled: Question Statement Backchannel Incomplete Highlights Fisher and Switchboard audio clips Words annotated with POS tags Sentence units labeled: Question Statement Backchannel Incomplete

Methodology Corpus XML Stream of words Corpus WAV Lexical and prosodic feature soup Word Features StatementQuestionMid-sentenceBackchannel

Effect of POS tags on ‘end of sentence’ detection Just post word POS tags don’t help “and so do other people” CC RB VB JJ NNS RB+VB VB+JJ VB RB+VB+JJ CC+RB+VB+JJ+NNS $POS+CC+RB+VB+JJ+NNS+$POS

Effect of POS tags on various Sentence-Unit classes “cs224s course rocks?” “cs224s course rocks.” “mhm”

Previous Sentence Label helps (SU following question is probably a Question) Length of unclassified contiguous word stream seen so far improves backchannel detection (since they are short)

Effect of prosodic features on improving ‘Question’ classification

Combining all features, we are able to get up to 99% accuracy on classifying a word as a “end of sentence unit” or not: However, lesser accuracy when trying to classify individual classes. Specifically, gives only 62% accuracy with ‘Questions’

References Enriching Speech Recognition With Automatic Detection of Sentence Boundaries and Disﬂuencies, Yang Liu, Elizabeth Shriberg, Andreas Stolcke, Dustin Hillard, Mari Ostendorf and Mary Harper Yang Liu, Elizabeth Shriberg, Andreas Stolcke, Barbara Peskin, Jeremy Ang, Dustin Hillard, Mari Ostendorf, Marcus Tomalin, Phil Woodland, and Mary Harper Structural Metatada Research in the EARS Program,. ICASSP Yang Liu, Elizabeth Shriberg, Andreas Stolcke, Dustin Hillard, Mari Ostendorf, Barbara Peskin, and Mary Harper The ICSI-SRI-UW Metadata Extraction System, ICSLP Snover, Matthew, Bonnie Dorr and Richard Schwartz A Lexically-Driven Algorithm for Disfluency Detection. Short Papers Proceedings of HLT-NAACL Boston: ACL Dr. Dan Jurafsky for encouragement and office hours Yun-Hsuan Sung for advice on how to proceed with this project Uriel Cohen Priva for assistance with obtaining the LDC2009T01 corpus Acknowledgements