| Challenge: | Traditionally, native speakers of a language have been asked to annotate a corpus in that language. |
| Approach: | They propose two annotation platforms that allow an English speaker to annotate names for any language without knowing the language. |
| Outcome: | The proposed annotations achieved state-of-the-art performance on two surprise languages and ten languages at TAC-KBP EDL2017. |
Similar Papers
Dragonfly: Advances in Non-Speaker Annotation for Low Resource Languages (2020.lrec-1)
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| Challenge: | Using semantic and contextual information, non-speakers of a language familiar with the Latin script can produce high quality named entity annotations to support construction of . name tagger. |
| Approach: | They propose a procedure for annotating low resource languages using Dragonfly that others can use. |
| Outcome: | The proposed procedure improves the performance of NER models on native speaker and non-speaker annotations in low resource languages. |
Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages (D19-61)
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| Challenge: | a novel name retrieval method is proposed for languages with no annotations or training data. |
| Approach: | They propose a method which relies on zero annotation or resources from the target language . they pre-train an orthographic encoder using Wikipedia inter-lingual links from dozens of languages . |
| Outcome: | The proposed method shows 11.6% improvement over state-of-the-art methods. |
Should All Cross-Lingual Embeddings Speak English? (2020.acl-main)
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| Challenge: | lexicon induction evaluation dictionaries are mostly between English and another language, and the English hub is selected by default as the hub . lexiconic embeddings are often learned with a two-step process, whether under bilingual or multilingual settings. |
| Approach: | They propose to use English as the hub language for lexicon induction evaluation . they also expand a standard English-centered evaluation dictionary collection to include all language pairs . |
| Outcome: | The proposed method can significantly improve lexicon induction performance over multiple languages. |
Rethinking Annotation: Can Language Learners Contribute? (2023.acl-long)
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| Challenge: | Researchers have traditionally recruited native speakers to provide annotations for benchmark datasets, but there are languages for which recruiting native speakers is difficult. |
| Approach: | They recruit 36 language learners and provide two types of additional resources and perform mini-tests to measure their language proficiency. |
| Outcome: | The proposed method improves learners' language proficiency in terms of vocabulary and grammar. |
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. |
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. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |
What data should I include in my POS tagging training set? (2025.findings-emnlp)
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| Challenge: | POS tagging is a crucial task for descriptive linguistics and language documentation . POS tags are not available in all languages, but are used for training sets for understudied languages . |
| Approach: | They compare POS tagging with in-context learning, active learning, and random sampling . they find that POS can deliver reasonable results for communities with limited resources . |
| Outcome: | The proposed training set for Indigenous and endangered languages performs better than random sampling. |
Unsupervised Cross-Lingual Representation Learning (P19-4)
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges (L18-1)
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| Challenge: | despite efforts at name tagging, there is limited understanding on the performance ceiling . despite the high-resource language, there are very few natural language processing tools available . |
| Approach: | They propose to use a machine learning model to identify Uyghur name tagger errors . they conclude that such a model is unlikely to be effective for Uygur, or low-resource languages . |
| Outcome: | The proposed model is unlikely to be effective for Uyghur, or low-resource languages in general, the authors argue . they show that the proposed model can be used for high-res languages with superficial features . |
Writing System and Speaker Metadata for 2,800+ Language Varieties (2022.lrec-1)
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| Challenge: | Currently, language technologies are easily available in only a small minority of the world's 7,000+ language varieties. |
| Approach: | They propose to use an open-source dataset to provide the writing system(s) for each of the 2,800+ languages used in the world today and an estimated speaker count for each. |
| Outcome: | The dataset provides the attested writing system(s) for each of these 2,800+ varieties, as well as an estimated speaker count for each variety. |