2017, Multi-turn, Goal-oriented, Frame-tracking(Dialog State Tracking)
This paper presents the Frames dataset, a corpus of 1369 human-human dialogues with an average of 15 turns per dialogue. We developed this dataset to study the role of memory in goal-oriented dialogue systems. Based on Frames, we introduce a task called frame tracking, which extends state tracking to a setting where several states are tracked simultaneously. We propose a baseline model for this task. We show that Frames can also be used to study memory in dialogue management and information presentation through natural language generation.
相关论文： A Frame Tracking Model for Memory-Enhanced Dialogue Systems
Dataset: http://datasets.maluuba.com/Frames
2017, Sarcasm
The use of irony and sarcasm in social media allows us to study them at scale for the first time. However, their diversity has made it difficult to construct a high-quality corpus of sarcasm in dialogue. Here, we describe the process of creating a largescale, highly-diverse corpus of online debate forums dialogue, and our novel methods for operationalizing classes of sarcasm in the form of rhetorical questions and hyperbole. We show that we can use lexico-syntactic cues to reliably retrieve sarcastic utterances with high accuracy. To demonstrate the properties and quality of our corpus, we conduct supervised learning experiments with simple features, and show that we achieve both higher precision and F than previous work on sarcasm in debate forums dialogue. We apply a weakly-supervised linguistic pattern learner and qualitatively analyze the linguistic differences in each class.
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
Dataset: https://nlds.soe.ucsc.edu/sarcasm2
IAC 2.0: https://nlds.soe.ucsc.edu/iac2
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