Word alignment plays a crucial role in several Natural Language Processing tasks, such as lexicon injection and cross-lingual label projection. The evaluation of word alignment systems relies heavily on manually-curated datasets, which are not always available, especially in mid- and low-resource languages. In order to address this limitation, we propose XL-WA, a novel entirely manually-curated evaluation benchmark for word alignment covering 14 language pairs. We illustrate the creation process of our benchmark and compare statistical and neural approaches to word alignment in both language-specific and zero-shot settings, thus investigating the ability of state-of-the-art models to generalize on unseen language pairs. We release our new benchmark at: https://github.com/SapienzaNLP/XL-WA.
XL-WA: a Gold Evaluation Benchmark for Word Alignment in 14 Language Pairs
Quochi V.;
2023
Abstract
Word alignment plays a crucial role in several Natural Language Processing tasks, such as lexicon injection and cross-lingual label projection. The evaluation of word alignment systems relies heavily on manually-curated datasets, which are not always available, especially in mid- and low-resource languages. In order to address this limitation, we propose XL-WA, a novel entirely manually-curated evaluation benchmark for word alignment covering 14 language pairs. We illustrate the creation process of our benchmark and compare statistical and neural approaches to word alignment in both language-specific and zero-shot settings, thus investigating the ability of state-of-the-art models to generalize on unseen language pairs. We release our new benchmark at: https://github.com/SapienzaNLP/XL-WA.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
