Graph Algorithms for Multiparallel Word Alignment (2021)

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.

## introduction word alignment is a challenging nlp task that plays an essential role in statistical machine translation and is useful for neural machine translation @xcite @xcite . other applications of word alignments include bilingual lexicon induction, annotation projection, and typological analysis @xcite @xcite . with the advent of deep learning, interest in word alignment initially decreased. however, recently a new wave of publications has again drawn attention to the task @xcite @xcite ). ## conclusion and future work we presented mpwa, a framework for leveraging multiparallel corpora for word alignment. we used two prediction methods, one based on recommender systems and one based on link prediction algorithms. by adding new alignment edges to the output of bilingual aligners, both methods show large improvements over the bilingual baselines, with absolute improvements of f 1 of up to 20%. we have also ported blinker and helfi word alignment gold standards to the parallel bible corpus in the hope that this will help foster more work on exploiting multiparallel corproa. future work. in this paper, we have mainly focused on adding new alignment edges to baseline word alignments based on properties of the multiparallel alignment graph. this increases recall, but can harm precision. in future work, we plan to expand on the possibility of deleting edges based on evidence from the multiparallel alignment graph (cf. 5.3.3), thereby potentially improving both precision and recall.