mlsanityData to 14 Oct 2026

A Discriminative Neural Model for Cross-Lingual Word Alignment

arXiv 1909.00444 ↗First posted 1 Sep 2019cs.CLcited by 14

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Abstract (arXiv)

We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (~1.7K-5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11-27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Sep 2019.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 3; 2024: 2; 2025: 1.

14 papers.

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