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Statistical Machine Translation adding Pattern-based Machine Translation in Chinese-English Translation
Abstract
We have developed a two-stage machine translation (MT) system. The first stage is a rule-based machine translation system. The second stage is a normal statistical machine translation system. For Chinese-English machine translation, first, we used a Chinese-English rule-based MT, and we ob- tained "ENGLISH" sentences from Chinese sentences. Sec- ond, we used a standard statistical machine translation. This means that we translated "ENGLISH" to English machine translation. W e believe this method has two advantages. One is that there are fewer unknown words. The other is that it produces structured or grammatically correct sentences. From the results of experiments, we obtained a BLEU score of 0.3151 in the BTEC-CE task using our proposed method. In contrast, we obtained a BLEU score of 0.3311 in the BTEC-CE task using a standard method (moses). This means that our proposed method was not as effective for the BTEC-CE task. Therefore, we will try to improve the perfor- mance by optimizing parameters.
... This idea was based on paper [3], [4], [5]. Similar studies were on paper [16], [17], [18], [15] [19] and [20]. [16] and [17] was Fresh-English translation and used SYSTRAN. ...
... [16] and [17] was Fresh-English translation and used SYSTRAN. [15] was Chineses-English translation for patent task and used SYSTRAN. [18] [19] [20] was Japanese-English translation for patent task. ...
We have evaluated the two-stage machine translation (MT) sys-tem. The first stage is a state-of-the-art trial rule-based machine translation system. The second stage is a normal statistical ma-chine translation system. For Japanese-English machine transla-tion, first, we used a Japanese-English rule-based MT, and we ob-tained "ENGLISH" sentences from Japanese sentences. Second, we used a standard statistical machine translation. This means that we translated "ENGLISH" to English machine translation. This method has an advantages that it produces grammatically correct sentences. From the results of experiments in the JE task, we obtained a BLEU score of 0.1996 using our proposed method. In contrast, we obtained a BLEU score of 0.1436 using a standard method. And for the EJ task, we obtained a BLEU score of 0.2775 using our proposed method. In contrast, we obtained a BLEU score of 0.0831 using a standard method. This means that our proposed method was effective for the JE and EJ task. However, there is a problem. The BLEU score was not so effective to measure the translation quality.
Patent claim sentences, despite their legal importance in patent documents, still pose difficulties for state-of-the-art statistical machine translation (SMT) systems owing to their extreme lengths and their special sentence structure. This paper describes a method for improving the translation quality of claim sentences, by taking into account the features specific to the claim sublanguage. Our method overcomes the issue of special sentence structure, by transferring the sublanguage-specific sentence structure from the source language to the target language, using a set of synchronous context-free grammar rules. Our method also overcomes the issue of extreme lengths by taking the sentence components to be the processing unit for SMT. An experiment demonstrates that our proposed method significantly improves the translation quality in terms of RIBES scores by over 25 points, in all of the four translation directions i.e., English-to-Japanese, Japanese-to-English, Chinese-to-Japanese and Japanese-to-Chinese directions. Alongside the improvement in RIBES scores, improvements of approximately five points in BLEU scores are observed for English-to-Japanese and Japanese-to-English directions, and that of 1.5 points are observed for Chinese-to-Japanese and Japanese-to-Chinese directions.
This paper gives an overview of the evaluation campaign results of the International Workshop on Spoken Language Translation (IWSLT) 2008 1 . In this workshop, we focused on the translation of spontaneous speech recorded in a real situation and the feasability of pivot-language-based trans-lation approaches. The translation directions were English into Chinese and vice versa for the Challenge Task, Chinese into English and English into Spanish for the Pivot Task, and Arabic, Chinese, Spanish into English for the standard BTEC Task. In total, 19 research groups building 58 MT engines participated in this year's event. Automatic and subjective evaluations were carried out in order to investigate the im-pact of spontaneity aspects of field data experiments on au-tomatic speech recognition (ASR) and machine translation (MT) system performance as well as the robustness of state-of-the-art MT systems towards speech-to-speech translation in real environments.
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