Cross-lingual Dependency Transfer : What Matters? Assessing the Impact of Pre- and Post-processing
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis
Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages…
Cross-Lingual NERCross-Lingual Transfernamed-entity-recognitionNamed Entity Recognition+3Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pret…
Cross-Lingual NERCross-Lingual TransferDependency ParsingDocument Classification+5Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing
This paper describes the first report on cross-lingual transfer for semantic dependency parsing. We present the insight that there are twodifferent kinds of cross-linguality, namely sur-face level and mantic level, and t…
Cross-Lingual TransferDependency ParsingSemantic Dependency ParsingRethinking what Matters: Effective and Robust Multilingual Realignment for Low-Resource Languages
Realignment is a promising strategy to improve cross-lingual transfer in multilingual language models. However, empirical results are mixed and often unreliable, particularly for typologically distant or low-resource lan…
Cross-Lingual TransferPretraining with Artificial Language: Studying Transferable Knowledge in Language Models
We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language. We design artificial languages with structural properties that mimic natural language, p…
Position