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Constrained Decoding for Cross-lingual Label Projection by Le, Duong Minh; Chen, Yang; Ritter, Alan; Xu, Wei is a scholarly article available to read on EtoBox.

What is Constrained Decoding for Cross-lingual Label Projection about?

Zero-shot cross-lingual transfer utilizing 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. Therefore, it is common to exploit translation and label projection to further improve the performance by (1) translating training data that is available in a high-resource language (e.g., English) together with the gold labels into low-resource languages, and/or (2) translating test data in low-resource languages to a high-source language to run inference on, then projecting the predicted span-level labels back onto the original test data. However, state-of-the-art marker-based label projection methods suffer from translation quality degradation due to the extra label markers injected in the input to the translation model. In this work, we explore a new direction that leverages constrained decoding for label projection to overcome the aforementioned issues. Our new method not only can preserve the quality of translated texts but also has

Author

Le, Duong Minh; Chen, Yang; Ritter, Alan; Xu, Wei

Published

2024

Language

EN