Ongoing study for enhancing chinese-spanish translation with morphology strategies

Chinese and Spanish have different morphology structures, which poses a big challenge for translating between this pair of languages. In this paper, we analyze several strategies to better generalize from the Chinese non-morphology-based language to the Spanish rich morphologybased language. Strategies use a first-step of Spanish morphology-based simplifications and a second-step of fullform generation. The latter can be done using a translation system or classification methods. Finally, both steps are combined either by concatenation in cascade or integration using a factored-based style. Ongoing experiments (based on the United Nations corpus) and their results are described.Postprint (published version

Capturing lexical variation in MT evaluation using automatically built sense-cluster inventories

The strict character of most of the existing Machine Translation (MT) evaluation metrics does not permit them to capture lexical variation in translation. However, a central issue in MT evaluation is the high correlation that the metrics should have with human judgments of translation quality. In order to achieve a higher correlation, the identification of sense correspondences between the compared translations becomes really important. Given that most metrics are looking for exact correspondences, the evaluation results are often misleading concerning translation quality. Apart from that, existing metrics do not permit one to make a conclusive estimation of the impact of Word Sense Disambiguation techniques into MT systems. In this paper, we show how information acquired by an unsupervised semantic analysis method can be used to render MT evaluation more sensitive to lexical semantics. The sense inventories built by this data-driven method are incorporated into METEOR: they replace WordNet for evaluation in English and render METEOR’s synonymy module operable in French. The evaluation results demonstrate that the use of these inventories gives rise to an increase in the number of matches and the correlation with human judgments of translation quality, compared to precision-based metrics

Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation

Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge

Towards String-to-Tree Neural Machine Translation

We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.Comment: Accepted as a short paper in ACL 201