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Dialogue Understanding

This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

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Given the transcript of a conversation along with speaker information of each constituent utterance, the utterance-level dialogue understanding (utterance-level dialogue understanding) task aims to identify the label of each utterance from a set of pre-defined labels that can be either a set of emotions, dialogue acts, intents etc. The figures above and below illustrate such conversations between two people, where each utterance is labeled by the underlying emotion and intent. Formally, given the input sequence of N number of utterances (u1, p1), (u2,p2),...., (uN,pN), where each utterance ui=ui,1,ui,2,.....,ui,T consists of T words ui,j and spoken by party pi, the task is to predict the label ei of each utterance ui. In this process, the classifier can also make use of the conversational context. There are also cases where not all the utterances in a dialogue have corresponding labels.

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