SEQZERO: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models
Open accessAbstract
Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing.The canonical utterance is often lengthy and complex due to the compositional structure of formal languages.Learning to generate such canonical utterance requires significant amount of data to reach high performance.Fine-tuning with only few-shot samples, the LMs can easily forget pretrained knowledge, overfit spurious biases, and suffer from compositionally out-of-distribution generalization errors.To tackle these issues, we propose a novel few-shot semantic parsing method -SEQZERO.SEQZERO decomposes the problem into a sequence of sub-problems, which correspond to the sub-clauses of the formal language.Based on the decomposition, the LMs only need to generate short answers using prompts for predicting sub-clauses.Thus, SEQZERO avoids generating a long canonical utterance at once.Moreover, SEQZERO employs not only a fewshot model but also a zero-shot model to alleviate the overfitting.In particular, SEQZERO brings out the merits from both models via ensemble equipped with our proposed constrained rescaling.SEQZERO achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split.1
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The shortcut learning problem has been observed in various natural language understanding tasks under the pretraining and fine-tuning paradigm, where models heavily rely on spurious correlations between input and labels in the fine-tuning data for prediction.
“The shortcut learning problem has been observed in various natural language understanding tasks under the pretraining and fine-tuning paradigm, where models heavily rely on spurious correlations between input and labels in the fine-tuning data for prediction [35,39,107,109,128,129].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [129]
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1910.13461 · Cited by 8,816 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · see citing works
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., & Auli, M. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-4009 · Cited by 3,538 · see citing works
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473 · see citing works
Language Models as Knowledge Bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834 · see citing works
Making Pre-trained Language Models Better Few-shot Learners
Gao, T., Fisch, A., & Chen, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.295 · Cited by 1,380 · see citing works
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.346 · Cited by 1,279 · see citing works
Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2023) · AI Open · doi:10.1016/j.aiopen.2023.08.012 · Cited by 1,124 · see citing works
Learning to parse database queries using inductive logic programming
Zelle, J. M., & Mooney, R. J. (1996) · Cited by 642 · see citing works
Eliciting Knowledge from Language Models Using Automatically Generated PromptsPreprint
Shin, T., Razeghi, Y., Logan, R. L., IV, Wallace, E., & Singh, S. (2020) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2010.15980 · Cited by 584 · see citing works
Information Extraction over Structured Data: Question Answering with Freebase
Yao, X., & Durme, B. V. (2014) · doi:10.3115/v1/p14-1090 · Cited by 513 · see citing works
Semantic Parsing via Paraphrasing
Berant, J., & Liang, P. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1133 · Cited by 504 · see citing works
Data Recombination for Neural Semantic Parsing
Jia, R., & Liang, P. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1002 · Cited by 441 · see citing works
Robust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00780 · Cited by 428 · see citing works
Semantic Parsing for Single-Relation Question Answering
Yih, W.-T., He, X., & Meek, C. (2014) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/v1/p14-2105 · Cited by 426 · see citing works
Coarse-to-Fine Decoding for Neural Semantic Parsing
Dong, L., & Lapata, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1068 · Cited by 378 · see citing works
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Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A., & Riedel, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01066 · Cited by 364 · see citing works
Learning a Neural Semantic Parser from User Feedback
Iyer, S. V., Konstas, I., Cheung, A., Krishnamurthy, J., & Zettlemoyer, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1089 · Cited by 343 · see citing works
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
Qin, G., & Eisner, J. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.410 · Cited by 342 · see citing works
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Lake, B. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05381 · Cited by 104 · see citing works
Mitchell, E., Lin, C., Bosselut, A., Finn, C., & Manning, C. D. (2021) · arXiv · doi:10.48550/arxiv.2110.11309 · Cited by 95 · see citing works
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Flagged first, then by citations
Research works printed but not linked: 3
- Jonathan Berant and Percy Liang. 2014. Semantic pars- ing via paraphrasing. In Proceedings of the 52nd An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1415- 1425.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Xuchen Yao and Benjamin Van Durme. 2014. Infor- mation extraction over structured data: Question an- swering with freebase. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 956-966.
Cited by
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KoBBQ: Korean Bias Benchmark for Question Answering
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Arora, A., Bhaisaheb, S., Nigam, H., Patwardhan, M. S., Vig, L., & Shroff, G. M. (2023) · GenBench Workshop on Generalisation (Benchmarking) in NLP (GenBench) · doi:10.18653/v1/2023.genbench-1.3 · Cited by 11 · see citing works
A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models
Song, Y., He, Y., Zhao, X.-F., Gu, H., Jiang, D., Yang, H., & Fan, L. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-4058-8 · Cited by 9 · see citing works
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张, 海. (2024) · Operations Research and Fuzziology · doi:10.12677/orf.2024.141008
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Self-citationPuzhou, W., Pan, H., & Bo, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227710
- Authors
- Yang, J., Jiang, H., Yin, Q., Zhang, D., Yin, B., & Yang, D.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Findings of the Association for Computational Linguistics: NAACL 2022
- Published
- 2022
- Pages
- 49-60
- OpenAlex
- W4280633590
- Cited by
- 15
- References
- 42
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 8
- FWCI
- 2.07
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: NAACL (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- few-shot learning, zero-shot learning, overfitting, ensemble learning, pre-trained language models
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