Comparative Study of Zero-Shot Cross-Lingual Transfer for Bodo POS and NER Tagging Using Gemini 2.0 Flash Thinking Experimental Model
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Abstract (arXiv)
Named Entity Recognition (NER) and Part-of-Speech (POS) tagging are critical tasks for Natural Language Processing (NLP), yet their availability for low-resource languages (LRLs) like Bodo remains limited. This article presents a comparative empirical study investigating the effectiveness of Google's Gemini 2.0 Flash Thinking Experiment model for zero-shot cross-lingual transfer of POS and NER tagging to Bodo. We explore two distinct methodologies: (1) direct translation of English sentences to Bodo followed by tag transfer, and (2) prompt-based tag transfer on parallel English-Bodo sentence pairs. Both methods leverage the machine translation and cross-lingual understanding capabilities of Gemini 2.0 Flash Thinking Experiment to project English POS and NER annotations onto Bodo text in CONLL-2003 format. Our findings reveal the capabilities and limitations of each approach, demonstrating that while both methods show promise for bootstrapping Bodo NLP, prompt-based transfer exhibits superior performance, particularly for NER. We provide a detailed analysis of the results, highlighting the impact of translation quality, grammatical divergences, and the inherent challenges of zero-shot cross-lingual transfer. The article concludes by discussing future research directions, emphasizing the need for hybrid approaches, few-shot fine-tuning, and the development of dedicated Bodo NLP resources to achieve high-accuracy POS and NER tagging for this low-resource language.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 6 Mar 2025.
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22 of 47 references.
References · 22
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
- 2019Cross-lingual Language Model Pretrainingcited by 1,088
- 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
- 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
- 2019How multilingual is Multilingual BERT?cited by 642
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- 2016Neural Architectures for Named Entity Recognitioncited by 548
- 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
- 2015An Introduction to Convolutional Neural Networkscited by 510
- 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
- 2013Exploiting Similarities among Languages for Machine Translationcited by 187
- 2019Emerging Cross-lingual Structure in Pretrained Language Modelscited by 180
- 2017Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Taggingcited by 71
- 2018Challenges of language technologies for the indigenous languages of the Americascited by 29
- 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 24
- 2024Part-of-Speech Tagger for Bodo Language using Deep Learning approachcited by 2
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