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Found 5 papers, 4 papers with code
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks.
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers
Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
Zero-shot Neural Transfer for Cross-lingual Entity Linking
To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language.
Neural Cross-Lingual Named Entity Recognition with Minimal Resources
To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order.
