Contextual Label Projection for Cross-Lingual Structured Prediction
arxiv(2023)
摘要
Label projection, which involves obtaining translated labels and texts
jointly, is essential for leveraging machine translation to facilitate
cross-lingual transfer in structured prediction tasks. Prior research exploring
label projection often compromise translation accuracy by favoring simplified
label translation or relying solely on word-level alignments. In this paper, we
introduce a novel label projection approach, CLaP, which translates text to the
target language and performs contextual translation on the labels using the
translated text as the context, ensuring better accuracy for the translated
labels. We leverage instruction-tuned language models with multilingual
capabilities as our contextual translator, imposing the constraint of the
presence of translated labels in the translated text via instructions. We
benchmark CLaP with other label projection techniques on zero-shot
cross-lingual transfer across 39 languages on two representative structured
prediction tasks - event argument extraction (EAE) and named entity recognition
(NER), showing over 2.4 F1 improvement for EAE and 1.4 F1 improvement for NER.
We further explore the applicability of CLaP on ten extremely low-resource
languages to showcase its potential for cross-lingual structured prediction.
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