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Published byJoan Fletcher Modified over 9 years ago
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Domain Act Classification using a Maximum Entropy model Lee, Kim, Seo (AAAI unpublished) Yorick Wilks Oxford Internet Institute and University of Sheffield www.dcs.shef.ac.uk
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Why are we reading this unpublished paper?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.
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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}]
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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)
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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.
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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
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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).
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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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