Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages (2025)

Abstract: Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for cross-lingual NER and can outperform multi-lingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we present a novel formalized projection approach of matching source entities with extracted target candidates. Through extensive experiments on two datasets spanning 57 languages, we demonstrated that our approach surpasses existing projection-based methods in low-resource settings. These findings highlight the robustness of projection-based data transfer as an alternative to model-based methods for cross-lingual named entity recognition in low-resource languages.

## introduction named entity recognition is well-studied in natural language processing (nlp), but remains a challenge for low-resource languages due to the lack of manual annotation @xcite . of the roughly 7,000 languages spoken worldwide, most are low-resource, with over 2,800 endangered @xcite . cross-lingual approaches present a promising solution to address the scarcity of labelled data in these languages. cross-lingual ner methods can be categorized into model transfer and data-based transfer approaches @xcite . model transfer approaches depend on the ability of multilingual models to convey task-specific knowledge across languages. data-based methods automate labelling through translation and annotation projection processes while leveraging advancements in multi-lingual language models to enable zero-shot cross-lingual transfer. this approach allows models trained in high-resource languages to identify and classify named entities in other languages without additional annotated data. additionally, categorization can be done through two approaches: translate-test, which labels original sentences in zero-shot settings, and translate-train, which generates labelled data to train a ner model. here we contribute to the field of cross-lingual ner by demonstrating the effectiveness of a databased cross-lingual transfer method that achieves comparable and, in some cases, higher performance of multilingual language models in low-and extremely low-resource language scenarios. our work focuses on the projection phase of cross-lingual ner pipelines, introducing two improvements to projection-based methods. first, we propose a method specifically designed to improve word-to-word alignments. second, we present a novel formalized projection approach of matching source entities with extracted target candidates. the proposed methods support translate-train and translate-test setups, achieving performance on par with model-based cross-lingual transfer techniques while offering greater flexibility. we evaluated our approach using the xtreme @xcite and @xcite datasets comprising 57 languages in total in translate-test settings. the source code and the evaluation results are provided in the github repository foot_0 . ## related work model transfer methods leverage the ability of models to transfer task-specific knowledge across languages. for example, multilingual models like mbert @xcite and xlm-roberta @xcite ) are trained on high-resource languages and applied to low-resource languages without modification. @xcite demonstrated improved performance when models were fine-tuned on labelled data or pre-trained on a related language. however, low-resource languages often lack sufficient data, and transfer quality diminishes when applied to very different target languages. data-based methods employ labelled datasets, often available in high-resource languages, to perform labelling tasks in the target language. they include fully artificial data generation, like mulda @xcite , and annotation projection methods. this paper focuses on the latter, which typically involves three steps: (i) translating the original sentence from the target (low-resource) to the source (high-resource) language, (ii) applying a ner model to the translated sentence, and (iii) projecting the labels back to the original sentence. while translation and ner use established models such as bert @xcite , many methods have been developed for the projection step. the first major group @xcite @xcite of projection methods is based on backtranslation, where labelled source sentences or their parts are translated back to the target language, preserving the labels. easyproject @xcite is a translate-train method that employs the insertion of special markers, specifically square brackets, around source entities. the marked sentence is then passed to the translation model, which independently translates the entire sentence and each source entity. afterwards, fuzzy string matching is used to project labels: for each substring in the back-translated sentence surrounded by markers, the method identifies the highest fuzzy match for the corresponding translation of the source entity and assigns the appropriate label. another type of projection method is based on word-to-word alignments @xcite @xcite @xcite . the general idea is to compute word-toword correspondence between words of a labelled sentence in a source language and an original sentence in a target language. the entity's label is projected onto target words that align with any of the entity's words. @xcite have shown that using contextualized neural networkbased aligners such as simalign @xcite or awesome @xcite is significantly more beneficial than statistical alignment tools like fastalign @xcite , but still can produce wrong alignments and therefore lead to projection errors. ## methodology our proposed approach focuses on projectionbased methods that involve word-to-word alignments. we present two improvements (see figure 1 ) to existing methods which are intended to be useful for languages that are under-presented in pre-trained language models. firstly, we investigate an alternative alignment direction to address the known issue of word-to-word alignment quality. secondly, we reformulate the annotation projection task as a bipartite matching problem between source entities and target candidates, using alignment-based matching scores to formalize the problem and eliminate reliance on heuristics, thereby facilitating method extension. ## experiments we performed an intrinsic evaluation of the efficiency of our approaches across a total of 57 languages using the xtreme @xcite ) (39 languages) and masakhaner2 datasets (excluding ghomálá and naijá languages due to limitations in translation model support -18 languages in total). this evaluation encompasses the full pipeline, considering both translation and source ner model performance. for a comparative analysis of existing and proposed approaches, we (re)implemented the aforementioned projection methods according to their original papers. in particular, we reimplemented the heuristic word-to-word alignment-based approach outlined by @xcite . we enhanced this heuristic by introducing a word count ratio threshold of 0.8 to better handle misaligned unitary words. additionally, we reimplemented the easyproject method, which performs back-translation of labelled source sentences, using original, fine-tuned by authors, nllb-200-3.3b 2 model. this back-translated output is then used for annotation projection, relying on word-to-word alignments computed between the original and labelled back-translated sentences in the same language (denoted as tgt2tgt). nllb200-3.3b 3 (costa-jussà et al., 2022) was employed as a translation model for all experiments. the xlm-r-large model 4 , fine-tuned on the english split of the conll2003 @xcite , served as both the source model and for target candidate extraction, as well as for model transfer experiments. we ignored misc entities predicted by this model in the first set of experiments since this class does not exist in the masakhaner2 and xtreme datasets. for computing word-to-word alignments, we used the original implementations of simalign and nonfinetuned awesome neural aligners with the default settings (with mbert model). as the evaluation involved full pipelines, the resulting metrics were influenced by both translation quality and the performance of the ner models. to ensure a fair and consistent comparison of the proposed methods, we employed the same models for translation and source labelling throughout all all models are from the hf hub 2 ychennlp/nllb-200-3.3b-easyproject 3 facebook/nllb-200-3.3b 4 facebookai/xlm-roberta-large-finetuned-conll03english experiments. for tasks involving the proposed integer linear programming (ilp) formulation of the projection problem, we utilized the previously described greedy approximation algorithm to derive solutions. evaluation results for the full pipelines are given in as shown in the ner model-based extraction (ner cand.) generally outperforms model transfer by effectively correcting labels for correctly predicted spans, resulting in greater accuracy particularly when model transfer mislabels these spans. it also surpasses the n-gram approach and achieves results comparable to model transfer because of more fine-grained candidates. the model transfer generally performs better on the xtreme dataset, but candidate matching methods surpass heuristic approaches in most of the 36 languages, except for bengali, kazakh, and swahili. the first may happen due to the model's exposure to these languages or their partial representations during pretraining, despite being finetuned only on english data. although the average score for the masakhaner2 dataset is modest, the proposed method performs better than heuristics in 10 languages and worse in 8 out of 18 total languages. the full list can be found in the appendix. this discrepancy may be attributed to the simpler morphological structures in the first group(where proposed methods perform better), while the second group, especially languages like xhosa and zulu @xcite , presents greater morphological complexity, including noun class systems and agreement patterns. the proposed method with target-to-target alignment direction generally does not outperform the source-to-target method, except for japanese, due to errors introduced during back-translation, highlighting a potential area for future research. additional experiments, described in the appendix, evaluate the performance of the projection step independently. formance on pre-labelled europarl parallel texts @xcite , excluding translation and source ner labelling errors. it highlights that candidate matching methods yield results comparable to or better than prior approaches. the ner-based target candidates approach underperforms due to imperfect spans but surpasses plain model transfer by correcting mislabeled spans via source entity projection. ## conclusion in this study, we presented novel annotation projection methods based on word-to-word alignments for cross-lingual ner. the idea to compute word-to-word alignments between the original and back-translated labelled sentences in the same language, aimed at enhancing the quality of these alignments, did not produce the desired outcomes. this approach encountered significant challenges, primarily due to errors that occurred during the back-translation process. in contrast, the proposed method of extracting candidates and matching them with source entities showed robust results. more specifically, the proposed formulation generally outperformed previous word-to-word alignment-based projection methods that relied on heuristics to deal with incorrect alignments. by using the same ner model for candidate ex-traction as in model transfer, the proposed approach can outperform model transfer. this is achieved by refining the labels for correctly predicted spans through projection from source entities. despite its advantages, the proposed approach remains heavily dependent on the quality of wordto-word alignments. however, the formulated ilp problem incorporates these alignments into matching scores that can be combined with other strategies using a weighted sum. our findings demonstrate that the projectionbased data transfer approach can be a robust alternative to model-based methods for cross-lingual named entity recognition in low-resource languages. future research could aim to improve candidate extraction and explore alternative matching costs in addition to the alignment-based one. the proposed formulation, in contrast to heuristic approaches, facilitates the integration of various scoring mechanisms, allowing for the fusion of different scores to effectively address the limitations associated with each individual method. moreover, exploring the usage of llms for the projection step in cross-lingual ner pipelines shows potential, indicating that the development of multilingual llms could help enhance the performance of ner tasks across diverse languages, especially when working with limited labelled data.