Assessing Data Efficiency in Task-Oriented Semantic Parsing

• URL: http://arxiv.org/abs/2107.04736v1
• Date: Sat, 10 Jul 2021 02:43:16 GMT
• Title: Assessing Data Efficiency in Task-Oriented Semantic Parsing
• Authors: Shrey Desai, Akshat Shrivastava, Justin Rill, Brian Moran, Safiyyah Saleem, Alexander Zotov, Ahmed Aly
• Abstract summary: We introduce a four-stage protocol which gives an approximate measure of how much in-domain "target" data a requires to achieve a certain quality bar. We apply our protocol in two real-world case studies illustrating its flexibility and applicability to practitioners in task-oriented semantic parsing.
• Score: 54.87705549021248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data efficiency, despite being an attractive characteristic, is often challenging to measure and optimize for in task-oriented semantic parsing; unlike exact match, it can require both model- and domain-specific setups, which have, historically, varied widely across experiments. In our work, as a step towards providing a unified solution to data-efficiency-related questions, we introduce a four-stage protocol which gives an approximate measure of how much in-domain, "target" data a parser requires to achieve a certain quality bar. Specifically, our protocol consists of (1) sampling target subsets of different cardinalities, (2) fine-tuning parsers on each subset, (3) obtaining a smooth curve relating target subset (%) vs. exact match (%), and (4) referencing the curve to mine ad-hoc (target subset, exact match) points. We apply our protocol in two real-world case studies -- model generalizability and intent complexity -- illustrating its flexibility and applicability to practitioners in task-oriented semantic parsing.

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