Papers with English parser
Unsupervised Cross-Lingual Adaptation of Dependency Parsers Using CRF Autoencoders (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on cross-lingual adaptation of dependency parsers without annotated target corpora focuses on discriminative source parser ignoring unannotated corporata . |
| Approach: | They propose to use unsupervised discriminative parsers to adapt dependency parser to unannotated target corpora without a supervised generative parsing method. |
| Outcome: | The proposed method significantly outperforms previous methods. |
Multilingual AMR Parsing with Noisy Knowledge Distillation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, and labeled graph. |
| Approach: | They propose to use existing English parser to learn and improve multilingual AMR parsers . their results show that noisy input and precise output are key to successful distillation . |
| Outcome: | The proposed model outperforms the current state-of-the-art English-only parser on four different languages. |
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost. |
| Approach: | They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages . |
| Outcome: | The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%. |
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | a new toolkit for localizing a semantic parser for a language is proposed . the proposed approach is based on a method for question answering systems . |
| Approach: | They propose a toolkit that leverages Neural Machine Translation systems to localize a semantic parser for a new language. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in 10 new languages . it can be deployed in restaurants and hotels in less than 24 hours . |