Recent advances of low-resource neural machine translation

In recent years, neural network-based machine translation (MT) approaches have steadily superseded the statistical MT (SMT) methods, and represents the current state-of-the-art in MT research. Neural MT (NMT) is a data-driven end-to-end learning protocol whose training routine usually requires a large amount of parallel data in order to build a reasonable-quality MT system. This is particularly problematic for those language pairs that do not have enough parallel text for training. In order to counter the data sparsity problem of the NMT training, MT researchers have proposed various strategies, e.g. augmenting training data, exploiting training data from other languages, alternative learning strategies that use only monolingual data. This paper presents a survey on recent advances of NMT research from the perspective of low-resource scenarios.

     

TermFinder: log-likelihood comparison and phrase-based statistical machine translation models for bilingual terminology extraction

Bilingual termbanks are important for many natural language processing applications, especially in translation workflows in industrial settings. In this paper, we apply a log-likelihood comparison method to extract monolingual terminology from the source and target sides of a parallel corpus. The initial candidate terminology list is prepared by taking all arbitrary n-gram word sequences from the corpus. Then, a well-known statistical measure (the Dice coefficient) is employed in order to remove any multi-word terms with weak associations from the candidate term list. Thereafter, the log-likelihood comparison method is applied to rank the phrasal candidate term list. Then, using a phrase-based statistical machine translation model, we create a bilingual terminology with the extracted monolingual term lists. We integrate an external knowledge source—the Wikipedia cross-language link databases—into the terminology extraction (TE) model to assist two processes: (a) the ranking of the extracted terminology list, and (b) the selection of appropriate target terms for a source term. First, we report the performance of our monolingual TE model compared to a number of the state-of-the-art TE models on English-to-Turkish and English-to-Hindi data sets. Then, we evaluate our novel bilingual TE model on an English-to-Turkish data set, and report the automatic evaluation results. We also manually evaluate our novel TE model on English-to-Spanish and English-to-Hindi data sets, and observe excellent performance for all domains.     

Integrating source-language context into phrase-based statistical machine translation

The translation features typically used in Phrase-Based Statistical Machine Translation (PB-SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling directly into log-linear PB-SMT can positively influence the weighting and selection of target phrases, and thus improve translation quality. In this contribution we present a revised, extended account of our previous work on using a range of contextual features, including lexical features of neighbouring words, supertags, and dependency information. We add a number of novel aspects, including the use of semantic roles as new contextual features in PB-SMT, adding new language pairs, and examining the scalability of our research to larger amounts of training data. While our results are mixed across feature selections, classifier hyperparameters, language pairs, and learning curves, we observe that including contextual features of the source sentence in general produces improvements. The most significant improvements involve the integration of long-distance contextual features, such as dependency relations in combination with part-of-speech tags in Dutch-to-English subtitle translation, the combination of dependency parse and semantic role information in English-to-Dutch parliamentary debate translation, or supertag features in English-to-Chinese translation.     

Augmenting training data with syntactic phrasal-segments in low-resource neural machine translation

Machine Translation - Neural machine translation (NMT) has emerged as a preferred alternative to the previous mainstream statistical machine translation (SMT) approaches largely due to its ability...