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- W4308671341 abstract "Word alignment is to find translationally equivalent words between source and target sentences. Previous work has demonstrated that self-training can achieve competitive word alignment results. In this paper, we propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. Specifically, source word and target word of each word pair aligned by the third-party aligner are trained to be close neighbors to each other in the contextualized embedding space when fine-tuning a pre-trained cross-lingual language model. Experiments on the benchmarks of various language pairs show that our approach can surprisingly do self-correction over the third-party supervision by finding more accurate word alignments and deleting wrong word alignments, leading to better performance than various third-party word aligners, including the currently best one. When we integrate all supervisions from various third-party aligners, we achieve state-of-the-art word alignment performances, with averagely more than two points lower alignment error rates than the best third-party aligner. We released our code at https://github.com/sdongchuanqi/Third-Party-Supervised-Aligner." @default.
- W4308671341 created "2022-11-14" @default.
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- W4308671341 date "2022-11-08" @default.
- W4308671341 modified "2023-09-27" @default.
- W4308671341 title "Third-Party Aligner for Neural Word Alignments" @default.
- W4308671341 doi "https://doi.org/10.48550/arxiv.2211.04198" @default.
- W4308671341 hasPublicationYear "2022" @default.
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