Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging (N19-1)
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| Challenge: | Low-resource language name tagging is an important but challenging task. |
| Approach: | They propose a neural architecture that leverages multi-level adversarial transfer to improve name tagging for low-resource languages. |
| Outcome: | The proposed approach outperforms previous approaches on CoNLL data sets. |
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| Challenge: | Existing studies on cross-lingual generalisability of large pre-trained models use English training data and test data in unseen languages. |
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A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers (D19-1)
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| Challenge: | Named entity recognition models rely on large amounts of labeled data, making them challenging to extend to new, lower-resource languages. |
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Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
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| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
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| Challenge: | a novel name retrieval method is proposed for languages with no annotations or training data. |
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| Challenge: | Existing studies have shown that multi-task learning can boost the performance of related tasks such as MT and abstractive text summarization. |
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TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods for named entity recognition are limited by noise in translation . Existing approaches to named entities recognition are mainly based on labeled data . |
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A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
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What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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| Challenge: | Named entity recognition models are challenging for languages with little training data. |
| Approach: | They propose a simple and efficient neural architecture for cross-lingual named entity recognition models. |
| Outcome: | The proposed model achieves competitive performance with the state-of-the-art on two transferable factors: sequential order and multilingual embedding. |
Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios (2020.emnlp-main)
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| Challenge: | a limited set of translations into one or more high-resource languages are available for POS tagging . a bi-LSTM architecture that uses contextualized word embeddings improves performance . |
| Approach: | They propose an unsupervised cross-lingual transfer approach for part-of-speech tagging . they use the Bible as parallel data to learn POS taggers for target languages . |
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