English/Veneto Resource Poor Machine Translation with STILVEN

The paper reports ongoing work for the implementation of a system for automatic translation from English-to-Veneto and viceversa. The system does not have parallel texts to work on because of the almost inexistence of such manual translations. The project is called STILVEN and is financed by the Regional Authorities of Veneto Region in Italy. After the first year of activities, we managed to produce a prototype which handles Venetian questions that have a structure very close to English. We will present problems related to Veneto, basic ideas, their implementatiion and results obtained

Phrase reordering for statistical machine translation based on predicate-argument structure

In this paper, we describe a novel phrase reordering model based on predicate-argument structure. Our phrase reordering method utilizes a general predicate-argument structure analyzer to reorder source language chunks based on predicate-argument structure. We explicitly model long distance phrase alignments by reordering arguments and predicates. The reordering approach is applied as a pre-processing step in training phase of a phrase-based statistical MT system. We report experimental results in the evaluation campaign of IWSLT 2006

Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation

In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English. JASS focuses on masking and reordering Japanese linguistic units known as bunsetsu, whereas ENSS is proposed based on phrase structure masking and reordering tasks. Experiments on ASPEC Japanese–English & Japanese–Chinese, Wikipedia Japanese–Chinese, News English–Korean corpora demonstrate that JASS and ENSS outperform MASS and other existing language-agnostic pre-training methods by up to +2.9 BLEU points for the Japanese–English tasks, up to +7.0 BLEU points for the Japanese–Chinese tasks and up to +1.3 BLEU points for English–Korean tasks. Empirical analysis, which focuses on the relationship between individual parts in JASS and ENSS, reveals the complementary nature of the subtasks of JASS and ENSS. Adequacy evaluation using LASER, human evaluation, and case studies reveals that our proposed methods significantly outperform pre-training methods without injected linguistic knowledge and they have a larger positive impact on the adequacy as compared to the fluency