Just Use XML: Revisiting Joint Translation and Label Projection
- URL: http://arxiv.org/abs/2603.12021v1
- Date: Thu, 12 Mar 2026 15:01:47 GMT
- Title: Just Use XML: Revisiting Joint Translation and Label Projection
- Abstract summary: LabelPigeon is a novel framework that jointly performs translation and label projection via XML tags.<n>We find that LabelPigeon outperforms baselines and actively improves translation quality in 11 languages.<n>Across 27 languages and three downstream tasks, we report substantial gains in cross-lingual transfer over comparable work, up to +39.9 F1 on NER.
- Score: 20.21221803121674
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Label projection is an effective technique for cross-lingual transfer, extending span-annotated datasets from a high-resource language to low-resource ones. Most approaches perform label projection as a separate step after machine translation, and prior work that combines the two reports degraded translation quality. We re-evaluate this claim with LabelPigeon, a novel framework that jointly performs translation and label projection via XML tags. We design a direct evaluation scheme for label projection, and find that LabelPigeon outperforms baselines and actively improves translation quality in 11 languages. We further assess translation quality across 203 languages and varying annotation complexity, finding consistent improvement attributed to additional fine-tuning. Finally, across 27 languages and three downstream tasks, we report substantial gains in cross-lingual transfer over comparable work, up to +39.9 F1 on NER. Overall, our results demonstrate that XML-tagged label projection provides effective and efficient label transfer without compromising translation quality.
Related papers
- The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks [0.0]
We study the effects of low-level design decisions on token-level XLT.<n>We find that, with optimized choices, XLT with WA offers performance at least comparable to that of marker-based methods.
arXiv Detail & Related papers (2025-05-15T17:10:50Z) - Constrained Decoding for Cross-lingual Label Projection [27.567195418950966]
Cross-lingual transfer using multilingual LLMs has become a popular learning paradigm for low-resource languages with no labeled training data.
However, for NLP tasks that involve fine-grained predictions on words and phrases, the performance of zero-shot cross-lingual transfer learning lags far behind supervised fine-tuning methods.
arXiv Detail & Related papers (2024-02-05T15:57:32Z) - Contextual Label Projection for Cross-Lingual Structured Prediction [103.55999471155104]
CLaP translates text to the target language and performs contextual translation on the labels using the translated text as the context.
We benchmark CLaP with other label projection techniques on zero-shot cross-lingual transfer across 39 languages.
arXiv Detail & Related papers (2023-09-16T10:27:28Z) - T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text
Classification [50.675552118811]
Cross-lingual text classification is typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest.
We propose revisiting the classic "translate-and-test" pipeline to neatly separate the translation and classification stages.
arXiv Detail & Related papers (2023-06-08T07:33:22Z) - Improving Self-training for Cross-lingual Named Entity Recognition with
Contrastive and Prototype Learning [80.08139343603956]
In cross-lingual named entity recognition, self-training is commonly used to bridge the linguistic gap.
In this work, we aim to improve self-training for cross-lingual NER by combining representation learning and pseudo label refinement.
Our proposed method, namely ContProto mainly comprises two components: (1) contrastive self-training and (2) prototype-based pseudo-labeling.
arXiv Detail & Related papers (2023-05-23T02:52:16Z) - Frustratingly Easy Label Projection for Cross-lingual Transfer [25.398772204761215]
A few efforts have utilized a simple mark-then-translate method to jointly perform translation and projection.
We present an empirical study across 57 languages and three tasks (QA, NER, and Event Extraction) to evaluate the effectiveness and limitations of both methods.
Our optimized version of mark-then-translate, which we call EasyProject, is easily applied to many languages and works surprisingly well, outperforming the more complex word alignment-based methods.
arXiv Detail & Related papers (2022-11-28T18:11:48Z) - CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual
Labeled Sequence Translation [113.99145386490639]
Cross-lingual NER can transfer knowledge between languages via aligned cross-lingual representations or machine translation results.
We propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER.
We adopt a multilingual labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence.
arXiv Detail & Related papers (2022-10-13T13:32:36Z) - FILTER: An Enhanced Fusion Method for Cross-lingual Language
Understanding [85.29270319872597]
We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning.
During inference, the model makes predictions based on the text input in the target language and its translation in the source language.
To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
arXiv Detail & Related papers (2020-09-10T22:42:15Z) - A Study of Cross-Lingual Ability and Language-specific Information in
Multilingual BERT [60.9051207862378]
multilingual BERT works remarkably well on cross-lingual transfer tasks.
Datasize and context window size are crucial factors to the transferability.
There is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
arXiv Detail & Related papers (2020-04-20T11:13:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.