Papers with Dialog State Tracking Challenge

1 papers
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding (N19-1)

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Challenge: Existing models that use contextual information of dialogues to improve spoken language understanding (SLU) select the wrong history when the histories are similar in content.
Approach: They propose time-aware models that automatically learn the latent time-decay function of the history without a manual time- decay.
Outcome: The proposed models achieve higher F1 scores than state-of-the-art models on a benchmark dataset .

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