# A Discriminative Neural Model for Cross-Lingual Word Alignment

arXiv: https://arxiv.org/abs/1909.00444 · HTML: https://mlsanity.com/paper/1909.00444/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [Neural semi-Markov CRF for Monolingual Word Alignment](https://mlsanity.com/paper/2106.02569/) (2021)
- [Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction](https://mlsanity.com/paper/2109.06798/) (2021)
- [WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction](https://mlsanity.com/paper/2306.05644/) (2023)
- [Contextual Label Projection for Cross-Lingual Structured Prediction](https://mlsanity.com/paper/2309.08943/) (2023)
- [Constrained Decoding for Cross-lingual Label Projection](https://mlsanity.com/paper/2402.03131/) (2024)
- [MirrorAlign: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning](https://mlsanity.com/paper/2102.04009/) (2021)
- [Word Alignment in the Era of Deep Learning: A Tutorial](https://mlsanity.com/paper/2212.00138/) (2022)
- [Gloss Alignment Using Word Embeddings](https://mlsanity.com/paper/2308.04248/) (2023)
- [MultiMUC: Multilingual Template Filling on MUC-4](https://mlsanity.com/paper/2401.16209/) (2024)
- [Multilingual Coreference Resolution in Multiparty Dialogue](https://mlsanity.com/paper/2208.01307/) (2022)
- [Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry](https://mlsanity.com/paper/2104.08721/) (2021)
- [TransAlign: Machine Translation Encoders are Strong Word Aligners, Too](https://mlsanity.com/paper/2510.27337/) (2025)

## 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.