Statistical Machine Translation Quality Research Articles

Improving English-Arabic statistical machine translation with morpho-syntactic and semantic word class

In this paper, we present a new method for the extraction and integrating of morpho-syntactic and semantic word classes in a statistical machine translation (SMT) context to improve the quality of English-Arabic translation. It can be applied across different statistical machine translations and with languages that have complicated morphological paradigms. In our method, we first identify morpho-syntactic word classes to build up our statistical language model. Then, we apply a semantic word clustering algorithm for English. The obtained semantic word classes are projected from the English side to the featured Arabic side. This projection is based on available word alignment provided by the alignment step using GIZA++ tool. Finally, we apply a new process to incorporate semantic classes in order to improve the SMT quality. We show its efficacy on small and larger English to Arabic translation tasks. The experimental results show that introducing morpho-syntactic and semantic word classes achieves 7.7% of relative improvement on the BLEU score.

Investigating the Relationship between Classification Quality and SMT Performance in Discriminative Reordering Models

Reordering is one of the most important factors affecting the quality of the output in statistical machine translation (SMT). A considerable number of approaches that proposed addressing the reordering problem are discriminative reordering models (DRM). The core component of the DRMs is a classifier which tries to predict the correct word order of the sentence. Unfortunately, the relationship between classification quality and ultimate SMT performance has not been investigated to date. Understanding this relationship will allow researchers to select the classifier that results in the best possible MT quality. It might be assumed that there is a monotonic relationship between classification quality and SMT performance, i.e., any improvement in classification performance will be monotonically reflected in overall SMT quality. In this paper, we experimentally show that this assumption does not always hold, i.e., an improvement in classification performance might actually degrade the quality of an SMT system, from the point of view of MT automatic evaluation metrics. However, we show that if the improvement in the classification performance is high enough, we can expect the SMT quality to improve as well. In addition to this, we show that there is a negative relationship between classification accuracy and SMT performance in imbalanced parallel corpora. For these types of corpora, we provide evidence that, for the evaluation of the classifier, macro-averaged metrics such as macro-averaged F-measure are better suited than accuracy, the metric commonly used to date.

Orthographic and morphological processing for English–Arabic statistical machine translation

Much of the work on statistical machine translation (SMT) from morphologically rich languages has shown that morphological tokenization and orthographic normalization help improve SMT quality because of the sparsity reduction they contribute. In this article, we study the effect of these processes on SMT when translating into a morphologically rich language, namely Arabic. We explore a space of tokenization schemes and normalization options. We also examine a set of six detokenization techniques and evaluate on detokenized and orthographically correct (enriched) output. Our results show that the best performing tokenization scheme is that of the Penn Arabic Treebank. Additionally, training on orthographically normalized (reduced) text then jointly enriching and detokenizing the output outperforms training on enriched text.

Integration of Multiple Bilingually-Trained Segmentation Schemes into Statistical Machine Translation

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair in which the source language is unsegmented and the target language segmentation is known. In the first step, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the proposed method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available monolingually built segmentation tools.