Graph Algorithms for Multiparallel Word Alignment
MCML Authors
Ayyoob Imani
Masoud Jalili Sabet
Dr.
* Former Member
Lütfi Kerem Senel
Dr.
* Former Member
Hinrich Schütze
Prof. Dr.
Principal Investigator
Abstract
Ayyoob Imani
Masoud Jalili Sabet
Dr.
* Former Member
Lütfi Kerem Senel
Dr.
* Former Member
Hinrich Schütze
Prof. Dr.
Principal Investigator
Abstract
With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently. Alignments are useful for typological research, transferring formatting like markup to translated texts, and can be used in the decoding of machine translation systems. At the same time, massively multilingual processing is becoming an important NLP scenario, and pretrained language and machine translation models that are truly multilingual are proposed. However, most alignment algorithms rely on bitexts only and do not leverage the fact that many parallel corpora are multiparallel. In this work, we exploit the multiparallelity of corpora by representing an initial set of bilingual alignments as a graph and then predicting additional edges in the graph. We present two graph algorithms for edge prediction: one inspired by recommender systems and one based on network link prediction. Our experimental results show absolute improvements in F1 of up to 28{%} over the baseline bilingual word aligner in different datasets.
inproceedings
EMNLP 2021
Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, Nov 07-11, 2021.
Authors
A. Imani • M. J. Sabet • L. K. Senel • P. Dufter • F. Yvon • H. SchützeLinks
DOIResearch Area
BibTeXKey: ISS+21