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We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, crossdomain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. 1 We describe an encoder-decoder framework for DST with hierarchical representations, which leads to 20% improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs. TurnUtterance and Annotation 1Hi can you book me a flight to Paris please. user.flight.book.object.equals .destination.equals.location.equals.Paris Sure, when and where will you depart? system.prompt.flight.book.object.equals .source .departureDateTime

This paper proposes a new method based on coreference-chains for extracting citations from research papers. To evaluate our method we created a corpus of citations comprised of citing papers for 4 cited papers. We analyze some phenomena of citations that are present in our corpus, and then evaluate our method against a cue-phrase-based technique. Our method demonstrates higher precision by 7-10%.

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