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Domain Act Classification using a Maximum Entropy model Lee, Kim, Seo (AAAI unpublished)

Domain Act Classification using a Maximum Entropy model Lee, Kim, Seo (AAAI unpublished). Yorick Wilks Oxford Internet Institute and University of Sheffield www.dcs.shef.ac.uk. Why are we reading this unpublished paper?.

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Domain Act Classification using a Maximum Entropy model Lee, Kim, Seo (AAAI unpublished)

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  1. Domain Act Classification using a Maximum Entropy modelLee, Kim, Seo(AAAI unpublished) Yorick Wilks Oxford Internet Institute and University of Sheffield www.dcs.shef.ac.uk

  2. Why are we reading this unpublished paper? • It proposes a pretty clear ML model using a standard method (ME) but which is novel in its application to dialogue and it is easy to see how to do better than them and gain some publishable traction. • Basically it tries to learn over DAs (Dialogue Acts) as well as conceptual content--of very much the type we propose. • It gets better DA figures than Webb by ML over both at once. • It suggests figures would be even better if they had measured the DEPENDENCE between the two.

  3. Sample of the annotation they need for their classifier. • When is the changed date? • [System: ask_ref+change-date] • It’s December 5th. • [User: response+change-date {date=December 5th}]

  4. Types of information annotated • System or User • Speech/Dialogue Act (from a set of 11, e.g. ask_if=YNQ) • Concept (from domain set: e.g. change-date, information-object, which function as n-place predicates) • Objects that are values of the predicate variable, e.g date=5 December) • ALSO actions in domain tied to instantiated predicates (e.g. Timeble:Insert:Date)

  5. The overall classification task • To derive a general classifier assigning speech acts AND concepts at once, treating them as independent. • EVEN THOUGH they can be seen not to be • I.e. SA/DAs based on local evidence and sequence AND • Concepts based on local evidence and sequence • Big fat ME expression to do these all in one. • DA element not very different from Webb method: both used lexical evidence, POS tags and n-grams.

  6. Key example of combination of local/global and SA/C information. • When is the changed date? • [System: ask_ref+change-date] • It’s December 5th. • [User: response+change-date {date=December 5th}] • Rather than • [User: inform+information-object {date=December 5th}] • BUT THIS CANT BE DONE WITHOUT LINKING SPEECH ACTS AND CONCEPTS

  7. Results • SA/DA rising to 93% precision after 1000 turns; • Concepts rising to 90% slightly later • DA set seems very small (how compare Webb and DAMSL? His figures less good).

  8. What can we take from this? • Cf. old arguments about limits on DA accuracy without semantic content. • Cf. Interactions local/global in Jelinek • BUT THEY DON’T ACUALLY DO IT, SO WHY THE BETTER FIGURES?

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