Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers

• URL: http://arxiv.org/abs/2210.11984v1
• Date: Fri, 21 Oct 2022 14:19:47 GMT
• Title: Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers
• Authors: Daniel Fern\'andez-Gonz\'alez
• Abstract summary: Task-oriented dialog systems, such as Apple Siri and Amazon Alexa, require a semantic parsing module in order to process user utterances and understand the action to be performed. We advance the research on shift-reduce semantic parsing for task-oriented dialog. In particular, we implement novel shift-reduces that rely on Stack-Transformers.
• Score: 0.40611352512781856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialog systems require a semantic parsing module in order to process user utterances and understand the action to be performed. This semantic parsing component was initially implemented by rule-based or statistical slot-filling approaches for processing simple queries; however, the appearance of more complex utterances demanded the application of shift-reduce parsers or sequence-to-sequence models. While shift-reduce approaches initially demonstrated to be the best option, recent efforts on sequence-to-sequence systems pushed them to become the highest-performing method for that task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialog. In particular, we implement novel shift-reduce parsers that rely on Stack-Transformers. These allow to adequately model transition systems on the cutting-edge Transformer architecture, notably boosting shift-reduce parsing performance. Additionally, we adapt alternative transition systems from constituency parsing to task-oriented parsing, and empirically prove that the in-order algorithm substantially outperforms the commonly-used top-down strategy. Finally, we extensively test our approach on multiple domains from the Facebook TOP benchmark, improving over existing shift-reduce parsers and state-of-the-art sequence-to-sequence models in both high-resource and low-resource settings.

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