Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
This addresses the data scarcity issue for low-resource language pairs in machine translation, though it appears incremental as it builds on existing bitext mining approaches.
The paper tackles the problem of obtaining high-quality parallel corpora for neural machine translation by mining pseudo-parallel sentences from paired documents, proposing computationally economical solutions that demonstrate success on the Tatoeba benchmark and downstream NMT tasks.
Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called "pseudo-parallel" sentences from paired documents in two languages. In this paper, we outline some problems with current methods, propose computationally economical solutions to those problems, and demonstrate success with novel methods on the Tatoeba similarity search benchmark and on a downstream task, namely NMT. We uncover the effect of resource-related factors (i.e. how much monolingual/bilingual data is available for a given language) on the optimal choice of bitext mining approach, and echo problems with the oft-used BUCC dataset that have been observed by others. We make the code and data used for our experiments publicly available.