Summary of Cross-lingual Back-parsing: Utterance Synthesis From Meaning Representation For Zero-resource Semantic Parsing, by Deokhyung Kang et al.
Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing
by Deokhyung Kang, Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study proposes Cross-Lingual Back-Parsing (CBP), a novel data augmentation methodology to enhance cross-lingual transfer for semantic parsing (SP) without requiring extensive annotations. Leveraging multilingual pretrained language models (mPLMs), CBP synthesizes target language utterances from source meaning representations. The methodology demonstrates substantial gains in the target language on two cross-language SP benchmarks, Mschema2QA and Xspider. Further analysis shows that the synthesized utterances have high slot value alignment rates while preserving semantic integrity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CBP helps computers understand sentences in different languages without needing a lot of labeled data for each language. This is important because it can make it easier to create systems that can understand and respond to questions in multiple languages. The study shows that CBP works well on two tests of cross-language understanding, and the generated sentences are accurate and meaningful. |
Keywords
» Artificial intelligence » Alignment » Data augmentation » Language understanding » Parsing » Semantic parsing
