Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder

• URL: http://arxiv.org/abs/2109.04500v1
• Date: Thu, 9 Sep 2021 18:23:45 GMT
• Title: Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder
• Authors: Elman Mansimov and Yi Zhang
• Abstract summary: We introduce a Recursive INsertion-based entity recognition (RINE) approach for semantic parsing in task-oriented dialog. RINE achieves state-of-the-art exact match accuracy on low- and high-resource versions of the conversational semantic parsing benchmark TOP. Our approach is 2-3.5 times faster than the sequence-to-sequence model at inference time.
• Score: 6.507504084891086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for semantic parsing in task-oriented dialog. Our model consists of an encoder network that incrementally builds the semantic parse tree by predicting the non-terminal label and its positions in the linearized tree. At the generation time, the model constructs the semantic parse tree by recursively inserting the predicted non-terminal labels at the predicted positions until termination. RINE achieves state-of-the-art exact match accuracy on low- and high-resource versions of the conversational semantic parsing benchmark TOP (Gupta et al., 2018; Chen et al., 2020), outperforming strong sequence-to-sequence models and transition-based parsers. We also show that our model design is applicable to nested named entity recognition task, where it performs on par with state-of-the-art approach designed for that task. Finally, we demonstrate that our approach is 2-3.5 times faster than the sequence-to-sequence model at inference time.

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