Word alignment for languages with scarce resources using bilingual corpora of other language pairs

Abstract

This paper proposes an approach to improve word alignment for languages with scarce resources using bilingual corpora of other language pairs. To perform word alignment between languages L1 and L2, we introduce a third language L3. Although only small amounts of bilingual data are available for the desired language pair L1-L2, large-scale bilingual corpora in L1-L3 and L2-L3 are available. Based on these two additional corpora and with L3 as the pivot language, we build a word alignment model for L1 and L2. This approach can build a word alignment model for two languages even if no bilingual corpus is available in this language pair. In addition, we build another word alignment model for L1 and L2 using the small L1-L2 bilingual corpus. Then we interpolate the above two models to further improve word alignment between L1 and L2. Experimental results indicate a relative error rate reduction of 21.30% as compared with the method only using the small bilingual corpus in L1 and L2.

Multi-Task Word Alignment Triangulation for Low-Resource Languages

“…With French as the pivot, our approach significantly outperforms the interpolation method of Wang et al (2006) on both alignment F-and Bleu scores. Somewhat surprisingly, we find that our approach is insensitive to the choice of pivot language.…”

“…Somewhat surprisingly, we find that our approach is insensitive to the choice of pivot language. Wang et al (2006) focus on learning a word alignment model without a source-target corpus. To do so, they assume access to both source-pivot and pivot-target bitexts on which they independently train a source-pivot word alignment model Θ sp and a pivot-target model Θ pt .…”

“…Our method subsumes the model interpolation approach of Wang et al (2006), who independently train these three models and then interpolate the source-target model with an approximate sourcetarget model, constructed by combining the sourcepivot and pivot-target models.…”

Cross-lingual alignment transfer: a chicken-and-egg story?

“…This can be interpreted as standard cross-lingual transfer fromT to T , but with the difference that the transferred knowledge is not monolingual but bilingual because of the interactions with S. With large data in both S-T andT -T , we call this scenario DIRECTED BRIDGE. This is for instance the context of Wang et al (2006)'s works on English-Japanese, using Chinese as a bridge lan-guage, but their cross-language word similarity does not exploit Chinese-Japanese linguistic similarity.…”

“…In this study, we explore ways to overcome this paradox and consider techniques for transferring alignment models or annotations across language pairs, a task that has hardly been addressed in literature (see however (Wang et al, 2006;Levinboim and Chiang, 2015)). Based on a high-level typology of cross-lingual transfer methodologies ( § 2), our contribution is to formalize realistic scenarios (defined in § 3) as well as some basic methodologies for projecting knowledge about bilingual alignments crosslinguistically ( § 4).…”