Christopher Lin
2021
Constrained Language Models Yield Few-Shot Semantic Parsers
Richard Shin
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Christopher Lin
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Sam Thomson
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Charles Chen
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Subhro Roy
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Emmanouil Antonios Platanios
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Adam Pauls
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Dan Klein
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Jason Eisner
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Benjamin Van Durme
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, language models are trained to generate natural language. To bridge the gap, we use language models to paraphrase inputs into a controlled sublanguage resembling English that can be automatically mapped to a target meaning representation. Our results demonstrate that with only a small amount of data and very little code to convert into English-like representations, our blueprint for rapidly bootstrapping semantic parsers leads to surprisingly effective performance on multiple community tasks, greatly exceeding baseline methods also trained on the same limited data.
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Co-authors
- Richard Shin 1
- Sam Thomson 1
- Charles Chen, Jr. 1
- Subhro Roy 1
- Emmanouil Antonios Platanios 1
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