Problem Solving of Non-equivalence Problems in English Into Indonesian Text

In the process of transferring one message of Source Language (SL) to Target Language (TL) in a translation must be careful by a translator, because one word may have more than one meaning. By knowing the possible meanings of a word, the meanings appropriately should be translated by a translator, and the readers will get the meaning and information of the target text. The equal meaning of source language to the target language is equivalnce, but non-equivalence occurs when the meaning in source language is not translated into the target language. There are many strategies to solve the problems of non-equivalence in Indonesian into English. A translator has a strategy to solve it. These strategies, that is, cultural, loan word, pharaphase, omission, semantically, hyponyms, etc

Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection

The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.Comment: 11 pages, The 55th Annual Meeting of the Association for Computational Linguistics (ACL), 201

Maintaining Source Language in Translating Holy Book: A Case of Translating Al-Qur‟an into Indonesian

Translation involves two or more languages in practice. This undoubtedly activates the conflict between the source and target language. When translator tries to help readers understand fully the concept of the source text, he will sacrifice the source language to maintain the target. In this terms translator will have diffciculties to balance between those two languages. Maintaining both is much more problematic in tranlsation practice. Shifting between theories of translations does not automatically help translator to mantain the contents of the text tranfered into target language. Distorting as well as inserting translator‘s idea then is impossible to avoid. Mastering culture, history, sociology an many other disciplines in both source and target language will then help very much the action of translating. The next problem then occurs if it is related to law and religious teaching. Since holy text is sacredly honoured by the believers, translator is potentially sentenced to be sinful and to lose his profession. The alternative solutions are leaving the original text (Arabic) put side by side with the translation, undertaking borrowing and calque, and annotating on the target text