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Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning
Language understanding (LU) and dialogue policy learning are two essenti...
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Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking
Dialogue state tracking (DST) is an essential component in task-oriented...
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Neural Semantic Role Labeling with Dependency Path Embeddings
This paper introduces a novel model for semantic role labeling that make...
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Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation
Open-domain multi-turn conversations mainly have three features, which a...
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Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues
Compared to traditional visual question answering, video-grounded dialog...
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TurnGPT: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog
Syntactic and pragmatic completeness is known to be important for turn-t...
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Feature Generation for Robust Semantic Role Labeling
Hand-engineered feature sets are a well understood method for creating r...
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Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its performance. Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.
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