Papers by Nathan Schucher
The Power of Prompt Tuning for Low-Resource Semantic Parsing (2022.acl-short)
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| Challenge: | Prompt tuning is an effective method for adapting pre-trained language models to downstream tasks. |
| Approach: | They propose to use prompt tuning for semantic parsing to map natural language utterances onto formal meaning representations. |
| Outcome: | The proposed method outperforms the fine-tuned model on low-resource splits of Overnight and TOPv2 on language representations with increasing model scale and target representations. |
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models (2021.emnlp-main)
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| Challenge: | Large pre-trained language models for textual data have an unconstrained output space . when fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. |
| Approach: | They propose a method for constraining auto-regressive decoders of language models through incremental parsing. |
| Outcome: | The proposed method can find valid output sequences by rejecting inadmissible tokens . it can be used on Spider and CoSQL text-to-SQl translation tasks . |