Papers with attention-based recurrent neural network
Zero Pronoun Resolution with Attention-based Neural Network (C18-1)
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| Challenge: | Recent neural network methods for zero pronoun resolution use contextual information to encode the zero pronomins since they contain no actual content. |
| Approach: | They propose a self-attention mechanism for encoding zero pronouns that focus on some informative parts of the associated texts and produce an efficient way of encode them. |
| Outcome: | The proposed model significantly surpasses existing Chinese zero pronoun resolution baseline systems. |
Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information (N18-1)
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| Challenge: | acoustic signals provide cues that help listeners disambiguate difficult parses . speech carries useful extra information associated with prosodic structure . |
| Approach: | They propose a model that integrates transcribed text and acoustic-prosodic features into a neural network that accepts text and prosodic feature. |
| Outcome: | The proposed model improves parse and disfluency detection scores over a strong text-only baseline. |
Modeling with Recurrent Neural Networks for Open Vocabulary Slots (C18-1)
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| Challenge: | Existing approaches to filling slots that take on values from a virtually unlimited set have been lacking in the natural language area. |
| Approach: | They propose a new attention-based recurrent neural network (RNN) model that captures the concept: Understanding the role of a word may vary according to how long a reader focuses on a particular part of . sentence. |
| Outcome: | The proposed model outperforms existing models with respect to discovering ‘open-vocabulary’ slots without any external information, such as a named entity database or knowledge base. |