Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning

• URL: http://arxiv.org/abs/2204.06748v1
• Date: Thu, 14 Apr 2022 04:06:39 GMT
• Title: Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning
• Authors: Geunseob Oh, Rahul Goel, Chris Hidey, Shachi Paul, Aditya Gupta, Pararth Shah, Rushin Shah
• Abstract summary: We propose a novel NAR semantic that introduces intent conditioning on the decoder. As the top-level intent governs the syntax and semantics of a parse, the intent conditioning allows the model to better control beam search. We evaluate the proposed NAR on conversational SP datasets, TOP & TOPv2.
• Score: 11.307865386100993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. While sequence-to-sequence-based auto-regressive (AR) approaches are common for conversational semantic parsing, recent studies employ non-autoregressive (NAR) decoders and reduce inference latency while maintaining competitive parsing quality. However, a major drawback of NAR decoders is the difficulty of generating top-k (i.e., k-best) outputs with approaches such as beam search. To address this challenge, we propose a novel NAR semantic parser that introduces intent conditioning on the decoder. Inspired by the traditional intent and slot tagging parsers, we decouple the top-level intent prediction from the rest of a parse. As the top-level intent largely governs the syntax and semantics of a parse, the intent conditioning allows the model to better control beam search and improves the quality and diversity of top-k outputs. We introduce a hybrid teacher-forcing approach to avoid training and inference mismatch. We evaluate the proposed NAR on conversational SP datasets, TOP & TOPv2. Like the existing NAR models, we maintain the O(1) decoding time complexity while generating more diverse outputs and improving the top-3 exact match (EM) by 2.4 points. In comparison with AR models, our model speeds up beam search inference by 6.7 times on CPU with competitive top-k EM.

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