Papers with SEQ2SEQ
Natural Language Interface for Databases Using a Dual-Encoder Model (C18-1)
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| Challenge: | Existing approaches to train data-driven natural language interfaces for databases are limited and lack of large datasets is probably the main reason for the lack of complex machine learning approaches. |
| Approach: | They propose a sketch-based two-step neural model for generating structured queries based on a user’s request in natural language. |
| Outcome: | The proposed model improves on two recent large datasets suitable for data-driven solutions for natural language interfaces for databases. |
Translating a Math Word Problem to a Expression Tree (D18-1)
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| Challenge: | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. |
| Approach: | They propose an equation normalization method to normalize duplicated equations and propose an ensemble model to combine their advantages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on the math word problem solving. |
Adaptive Parameterization for Neural Dialogue Generation (D19-1)
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| Challenge: | Existing models of open-domain dialogue generate responses based on sequence-to-sequence paradigms. |
| Approach: | They propose an Adaptive Neural Dialogue generation model which manages various conversations with conversation-specific parameterization. |
| Outcome: | The proposed model performs better on a large-scale conversational dataset. |