2026-05-10T23:15:33Zhttps://iris.cnr.it/oai/requestoai:iris.cnr.it:20.500.14243/4792412025-01-24T23:20:40Zcom_20.500.14243_46com_20.500.14243_21col_20.500.14243_47ou_ou239
XL-WA: a Gold Evaluation Benchmark for Word Alignment in 14 Language Pairs
Martelli F.
Bejgu A. S.
Campagnano C.
Cibej J.
Costa R.
Gantar A.
Kallas J.
Koeva S.
Koppel K.
Krek S.
Langemets M.
Lipp V.
Nimb S.
Olsen S.
Pedersen B. S.
Quochi V.
Salgado A.
Simon L.
Tiberius C.
Urena-Ruiz R. -J.
Navigli R.
Federico Boschetti, Gianluca E. Lebani, Bernardo Magnini, Nicole Novielli
Martelli, F.
Bejgu, A. S.
Campagnano, C.
Cibej, J.
Costa, R.
Gantar, A.
Kallas, J.
Koeva, S.
Koppel, K.
Krek, S.
Langemets, M.
Lipp, V.
Nimb, S.
Olsen, S.
Pedersen, B. S.
Quochi, V.
Salgado, A.
Simon, L.
Tiberius, C.
Urena-Ruiz, R. -J.
Navigli, R.
Deep Learning
Multilinguality
Natural Language Processing
Word Alignment
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.
2023
info:eu-repo/semantics/conferenceObject
https://hdl.handle.net/20.500.14243/479241
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85181170710
eng
ispartofbook:Proceedings of the Ninth Italian Conference on Computational Linguistics
9th Italian Conference on Computational Linguistics, CLiC-it 2023
volume:3596
numberofpages:9
serie:CEUR WORKSHOP PROCEEDINGS
info:eu-repo/semantics/openAccess
CEUR-WS
license:Creative commons
license uri:http://creativecommons.org/licenses/by/4.0/