Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog

• URL: http://arxiv.org/abs/2104.04923v1
• Date: Sun, 11 Apr 2021 05:44:35 GMT
• Title: Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
• Authors: Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan and Marjan Ghazvininejad
• Abstract summary: We propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. By combining non-autoregressive prediction with convolutional neural networks, we achieve significant latency gains and parameter size reduction.
• Score: 22.442123799917074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational use cases has been stymied by higher compute requirements and thus higher latency. In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. By combining non-autoregressive prediction with convolutional neural networks, we achieve significant latency gains and parameter size reduction compared to traditional RNN models. Our novel architecture achieves up to an 81% reduction in latency on TOP dataset and retains competitive performance to non-pretrained models on three different semantic parsing datasets. Our code is available at https://github.com/facebookresearch/pytext

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