Black-box machine translation systems have proven incredibly useful for a variety of applications yet by design are hard to adapt, tune to a specific domain, or build on top of. In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification. We first propose a method to automatically generate a large in-domain paraphrase corpus through back-translation with a black-box MT system, which is used to train a paraphrase model that “simplifies” the original sentence to be more conducive for translation. The model is used to preprocess source sentences of multiple low-resource language pairs. We show that this preprocessing leads to better translation performance as compared to non-preprocessed source sentences. We further perform side-by-side human evaluation to verify that translations of the simplified sentences are better than the original ones. Finally, we provide some guidance on recommended language pairs for generating the simplification model corpora by investigating the relationship between ease of translation of a language pair (as measured by BLEU) and quality of the resulting simplification model from back-translations of this language pair (as measured by SARI), and tie this into the downstream task of low-resource translation.

Statistical phrase-based translation learns translation rules from bilingual corpora, and has traditionally only used monolingual evidence to construct features that rescore existing translation candidates.In this work, we present a semi-supervised graph-based approach for generating new translation rules that leverages bilingual and monolingual data.The proposed technique first constructs phrase graphs using both source and target language monolingual corpora.Next, graph propagation identifies translations of phrases that were not observed in the bilingual corpus, assuming that similar phrases have similar translations.We report results on a large Arabic-English system and a medium-sized Urdu-English system.Our proposed approach significantly improves the performance of competitive phrasebased systems, leading to consistent improvements between 1 and 4 BLEU points on standard evaluation sets.Source!Target! el gato! los gatos!un gato! cat! the cat! the cats! a cat! Target!Prob.! the cat! 0.7! cat! 0.15! …! …! felino!canino!el perro!Target!Prob.! canine!0.6!dog! 0.3!…! …! Target!Prob.! the cats!0.8! cats! 0.1!…! …

We present power low rank ensembles (PLRE), a flexible framework for n-gram language modeling where ensembles of low rank matrices and tensors are used to obtain smoothed probability estimates of words in context.Our method can be understood as a generalization of ngram modeling to non-integer n, and includes standard techniques such as absolute discounting and Kneser-Ney smoothing as special cases.PLRE training is efficient and our approach outperforms stateof-the-art modified Kneser Ney baselines in terms of perplexity on large corpora as well as on BLEU score in a downstream machine translation task.

Data-driven refinement of non-terminal categories has been demonstrated to be a reliable technique for improving mono-lingual parsing with PCFGs. In this pa-per, we extend these techniques to learn latent refinements of single-category syn-chronous grammars, so as to improve translation performance. We compare two estimators for this latent-variable model: one based on EM and the other is a spec-tral algorithm based on the method of mo-ments. We evaluate their performance on a Chinese–English translation task. The re-sults indicate that we can achieve signifi-cant gains over the baseline with both ap-proaches, but in particular the moments-based estimator is both faster and performs better than EM. 1