| Challenge: | Recent work has found success with machine translation or zero-shot methods . however, these approaches can struggle to model how native speakers ask questions . |
| Approach: | They propose a meta-learning algorithm to leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. |
| Outcome: | The proposed approach trains a parser with maximum sample efficiency in six languages on ATIS. |
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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. |
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing (2023.tacl-1)
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| Challenge: | Existing work on cross-lingual semantic parsing has focused on English . a few-shot approach to parse from natural languages is comparatively unexplored . |
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Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing (2022.acl-long)
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Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
| Challenge: | Meta-learning can help overcome resource scarcity in cross-lingual NLP problems . pre-training of models requires large annotated training sets for the task at hand . |
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Zero-Shot Cross-Lingual Transfer with Meta Learning (2020.emnlp-main)
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| Challenge: | There are more than 7,000 languages spoken in the world, over 90 of which have more than 10 million native speakers each. |
| Approach: | They propose to use meta-learning to train a model on multiple languages at the same time . they use standard supervised, zero-shot cross-lingual, and few-shot crosses-lingual settings for different natural language understanding tasks. |
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| Challenge: | Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French. |
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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. |
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Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
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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. |
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Bootstrapping a Crosslingual Semantic Parser (2020.findings-emnlp)
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| Challenge: | Recent advances in semantic parsing are limited to English but professional translation can be prohibitively expensive. |
| Approach: | They adapt a semantic parser trained on a single language to new languages and multiple domains with minimal annotation. |
| Outcome: | The proposed approach achieves parsing accuracy within 2% of translation using only 50% of training data. |
Few-Shot Semantic Parsing for New Predicates (2021.eacl-main)
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| Challenge: | a recent study shows that state-of-the-art neural semantic parsers are less accurate when there is only a handful of utterance-logical form pairs per predicate. |
| Approach: | They propose to use a meta-learning method to train a few-shot learning problem . they also propose to regularize attention scores with alignment statistics and apply a smoothing technique . |
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