TMX markup: a challenge when adapting SMT to the localisation environment

Translation memory (TM) plays an important role in localisation workflows and is used as an efficient and fundamental tool to carry out translation. In recent years, statistical machine translation (SMT) techniques have been rapidly developed, and the translation quality and speed have been significantly improved as well. However,when applying SMT technique to facilitate post-editing in the localisation industry, we need to adapt SMT to the TM data which is formatted with special mark-up. In this paper, we explore some issues when adapting SMT to Symantec formatted TM data. Three different methods are proposed to handle the Translation Memory eXchange (TMX) markup and a comparative study is carried out between them. Furthermore, we also compare the TMX-based SMT systems with a customised SYSTRAN system through human evaluation and automatic evaluation metrics. The experimental results conducted on the French and English language pair show that the SMT can perform well using TMX as input format either during training or at runtime

Improving Statistical Machine Translation Through N-best List

Statistical machine translation (SMT) is a method of translating from one natural language (NL) to another using statistical models generated from examples of the NLs. The quality of translation generated by SMT systems is competitive with other premiere machine translation (MT) systems and more improvements can be made. This thesis focuses on improving the quality of translation by re-ranking the n-best lists that are generated by modern phrase-based SMT systems. The n-best lists represent the n most likely translations of a sentence. The research establishes upper and lower limits of the translation quality achievable through re-ranking. Three methods of generating an n-gram language model (LM) from the n-best lists are proposed. Applying the LMs to re-ranking the n-best lists results in improvements of up to six percent in the Bi-Lingual Evaluation Understudy (BLEU) score of the translation

Statistical machine translation system and computational domain adaptation

Statističko strojno prevođenje temeljeno na frazama jedan je od mogućih pristupa automatskom strojnom prevođenju. U radu su predložene metode za poboljšanje kvalitete strojnog prijevoda prilagodbom određenih parametara u modelu sustava za statističko strojno prevođenje. Ideja rada bila jest izgraditi sustave za statističko strojno prevođenje temeljeno na frazama za hrvatski i engleski jezik. Sustavi su trenirani za dva jezična smjera, na dvije domene, na paralelnim korpusima različitih veličina i obilježja za hrvatsko-engleski i englesko-hrvatski jezični par, nakon čega proveden postupak ugađanja sustava. Istraženi su hibridni sustavi koji objedinjuju značajke obiju domena. Time je ispitan izravan utjecaj adaptacije domene na kvalitetu automatskog strojnog prijevoda hrvatskog jezika, a nova saznanja mogu koristiti pri izgradnji novih sustava. Provedena je automatska i ljudska evaluacija (vrednovanje) strojnih prijevoda, a dobiveni rezultati uspoređeni su s rezultatima strojnih prijevoda dobivenih primjenom postojećih web servisa za statističko strojno prevođenje.Phrase-based statistical machine translation is one of possible automatic machine translation approaches. This work proposes methods for increasing the quality of machine translation by adapting certain parameters in the statistical machine translation model. The idea was to build phrase-based statistical machine translation systems for Croatian and English language. The systems were be trained for two directions, on two domains, on parallel corpora of different sizes and characteristics for Croatian-English and English-Croatian language pair, after which the tuning procedure was conducted. Afterwards, hybrid systems which combine features of both domains were investigated. Thereby the direct impact of domain adaptation on the quality of automatic machine translation of Croatian language was explored, whereas new findings can be utilised for building new systems. Automatic and human evaluation of machine translations were carried out, while obtained results were compared with results obtained from applying existing statistical machine translation web services

Retro-BLEU: Quantifying Chemical Plausibility of Retrosynthesis Routes through Reaction Template Sequence Analysis

Computer-assisted methods have emerged as valuable tools for retrosynthesis analysis. However, quantifying the plausibility of generated retrosynthesis routes remains a challenging task. We introduce Retro-BLEU, a statistical metric adapted from the well-established BLEU score in machine translation, to evaluate the plausibility of retrosynthesis routes based on reaction template sequences analysis. We demonstrate the effectiveness of Retro-BLEU by applying it to a diverse set of retrosynthesis routes generated by state-of-the-art algorithms and compare the performance with other evaluation metrics. The results show that Retro-BLEU is capable of differentiating between plausible and implausible routes. Furthermore, we provide insights into the strengths and weaknesses of Retro-BLEU, paving the way for future developments and improvements in this field

Experiments on domain adaptation for English-Hindi SMT

Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for English—Hindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline

Sentence similarity-based source context modelling in PBSMT

Target phrase selection, a crucial component of the state-of-the-art phrase-based statistical machine translation (PBSMT) model, plays a key role in generating accurate translation hypotheses. Inspired by context-rich word-sense disambiguation techniques, machine translation (MT) researchers have successfully integrated various types of source language context into the PBSMT model to improve target phrase selection. Among the various types of lexical and syntactic features, lexical syntactic descriptions in the form of supertags that preserve long-range word-to-word dependencies in a sentence have proven to be effective. These rich contextual features are able to disambiguate a source phrase, on the basis of the local syntactic behaviour of that phrase. In addition to local contextual information, global contextual information such as the grammatical structure of a sentence, sentence length and n-gram word sequences could provide additional important information to enhance this phrase-sense disambiguation. In this work, we explore various sentence similarity features by measuring similarity between a source sentence to be translated with the source-side of the bilingual training sentences and integrate them directly into the PBSMT model. We performed experiments on an English-to-Chinese translation task by applying sentence-similarity features both individually, and collaboratively with supertag-based features. We evaluate the performance of our approach and report a statistically significant relative improvement of 5.25% BLEU score when adding a sentence-similarity feature together with a supertag-based feature