| Challenge: | Recent years have seen an increasing number of applications that have a natural language interface, such as chatbots or "intelligent personal assistants" |
| Approach: | They propose a new training algorithm that trains a semantic parser on examples from a set of source domains and augment it with features and a logical form candidate filtering logic to support zero-shot adaptation. |
| Outcome: | The proposed framework performs better than a non-adapted parser with features and logical form candidate filtering logic. |
Similar Papers
On The Ingredients of an Effective Zero-shot Semantic Parser (2022.acl-long)
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| Challenge: | Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterrances to improve linguistic diversity. |
| Approach: | They propose to bridge gaps between canonical and real-world user-issued examples by using stronger paraphrasers and improved grammars. |
| Outcome: | The proposed model achieves strong performance on two semantic parsing benchmarks with zero labeled data. |
Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing (D18-1)
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| Challenge: | Existing methods for training semantic parsers in new domains require expensive supervision and lack the ability to generalize to new domain. |
| Approach: | They propose a zero-shot approach to parsing utterances in unseen domains . they map an utterant to an abstract, domain independent, logical form and replace slots with KB constants based on lexical alignment scores and global inference . |
| Outcome: | The proposed model achieves 53.4% accuracy on 7 domains in the OVERNIGHT dataset, significantly better than other zero-shot baselines and performs as good as a parser trained on over 30% of the target domain examples. |
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models (2023.emnlp-main)
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| Challenge: | Existing LLMs cannot generalize to domain-specific parsing tasks in a zero-shot setting. |
| Approach: | They propose a task-oriented parsing method that decomposes parse problem into abstractive and extractive question-answering problems. |
| Outcome: | The proposed method decomposes a parsing problem into abstractive and extractive question-answering (QA) problems. |
Zero-Shot Cross-lingual Semantic Parsing (2022.acl-long)
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| Challenge: | Recent work in cross-lingual semantic parsing assumes access to high-quality machine translation systems and word alignment tools. |
| Approach: | They propose a multi-task encoder-decoder model to transfer parsing knowledge to additional languages using only English-logical form paired data and in-domain natural language corpora. |
| Outcome: | The proposed model performs significantly above translation-based baselines and competes with the supervised upper-bound. |
ZeLa: Advancing Zero-Shot Multilingual Semantic Parsing with Large Language Models and Chain-of-Thought Strategies (2024.lrec-main)
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| Challenge: | Existing approaches to augment multilingual datasets with labeled English data are lacking in annotated data. |
| Approach: | They propose a framework to augment English data and then use it to train parsers . they propose to use multilingual chain-of-thought prompting techniques to augment other languages' data . |
| Outcome: | The proposed framework augments English data in other languages and trains them with no demonstration samples in target languages. |
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing training data is limited for languages other than English, so is the performance of the developed parsers. |
| Approach: | They propose to apply a pre-trained multilingual model to Italian, German and Dutch parsers where only a small number of manually annotated parses are available. |
| Outcome: | The proposed model improves on six parsers in English and Italian, German and Dutch, with the addition of universal dependency relations and universal POS tags as model-agnostic features. |
Grounded Adaptation for Zero-shot Executable Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing semantic parsers are usually engineered for each application environment, but they struggle when deployed to a new database. |
| Approach: | They propose a method to adapt existing semantic parsers to new environments . they propose combining a forward semantic parsed with a backward utterance generator to synthesize data in the new environment and select cycle-consistent examples to adapt the parser. |
| Outcome: | The proposed procedure outperforms data-augmentation and improves execution accuracy on the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks. |
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains (2024.eacl-short)
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| Challenge: | Recent work has shown that large language models can generate zero-shot summaries without explicit supervision that are often comparable or even preferred to manually composed reference summary. |
| Approach: | They evaluate large language models (LLMs) that generate zero-shot summaries without explicit supervision that are often comparable to manual reference summary . they acquire annotations from domain experts to identify inconsistencies in summaires and categorize errors. |
| Outcome: | The proposed model outperforms fine-tuned models in biomedical articles and legal bills across specialized domains. |
Meta-Learning for Domain Generalization in Semantic Parsing (2021.naacl-main)
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| Challenge: | Existing approaches to parsing use standard supervised learning, but little attention has been given to domain generalization. |
| Approach: | They propose a meta-learning framework which targets zero-shot domain generalization for semantic parsing. |
| Outcome: | The proposed framework significantly boosts parser performance on English and Chinese spider datasets. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
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Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |