Proceedings - Translation Concepts

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MuTra 2006 – Audiovisual Translation Scenarios: Conference Proceedings Stephen Armstrong & Colm Caffrey & Marian Flanagan English subtitle. Table 6 shows examples of how the system correctly translates short subtitles, demonstrating the system’s ability to segment sentences correctly and to reuse the chunks for different input. English Original Subtitle German Original Subtitle German EBMT Subtitle English Original Subtitle German Original Subtitle German EBMT Subtitle English Original Subtitle German Original Subtitle German EBMT Subtitle What’s the matter, baby? Was ist los, Baby? Was ist los, Baby? I don’t know Ich weiβ nicht. Ich weiβ nicht. I was grateful Ich war dankbar Ich war dankbar Tab. 6: short segments translated correctly Given the fact we are in the first stages of evaluation, we are aware of some problems that exist with the chunking algorithm, the recombination stage and the dictionary generated during runtime. The evaluation has helped us address lexical errors in the system, such as missing words and mistranslations, both of which can be seen in Table 7 below. We see that some words, like “dig” and “wide”, are apparently not being found in the dictionary, where “as” in mistranslated as “ace”. English Original Subtitle German Original Subtitle German EBMT Subtitle to dig it twice as wide. Doppelt so groβ graben. An dig sie zweimal ace wide. Tab. 7: Sentence 43, Set 2 Problems with the chunking phase of the system have also become evident, where a chunk from the English subtitle is mismatched with a chunk from the German sentence, as in the example given in Table 8. English Original Subtitle German Original Subtitle German EBMT Subtitle Now, Mr. Ewell, can you... Nun, Mr. Ewell, können sie... Nun, ich habe, können sie... Tab. 8: sentence 44, Set 1 10
MuTra 2006 – Audiovisual Translation Scenarios: Conference Proceedings Stephen Armstrong & Colm Caffrey & Marian Flanagan Here we see that “Mr. Ewell” has been falsely aligned with the chunk “ich habe”, which has wrongly replaced Mr. Ewell in the EBMT output. This sentence scored a 3 for intelligibility (see Table 1) from both subjects and a 3 from one and a 4 from the other for accuracy (see Tab. 2). 6 Future Work 6.1 Pilot Study Our next stage of evaluation will involve a pilot study into the acceptance of EBMT subtitles. A selection of short clips, subtitled in German, will be shown to native German speakers. The clips will consist of segments from English and Japanese language films, helping us ascertain whether source language knowledge has an effect on the acceptability levels of EBMT subtitles. The subtitled clips will be shown to participants and they will be asked to evaluate the subtitles. Some clips will present a random mixture of EBMT subtitles and human output, while others will be subtitled entirely with EBMT output and others with only human output. The random mixture will allow us, but not the viewer, to know which subtitles are human produced and which are not. It will enable us to note any major differences in their acceptability. Questionnaires and retrospective interviews will be carried out with participants to garner their opinions on the subtitle quality and acceptability. A screen capture button will enable participants to take a freeze-frame still of any sections of a clip they find surprising or of poor quality and will be a useful reminder for the interview stage. 6.2 Corpus selection and Correlation with Automatic MT Metrics We intend to carry out an investigation into the effects the corpus used to train our EBMT system has on the quality of the subtitles produced. To do this, our system will be trained on a variety of corpora, including subtitles only, bonus material only, a combination of the two, as well as the use of heterogeneous material (Armstrong, et al., forthcoming). This will help to clarify whether it is more effective to train the system on a smaller amount of specific data, or a large amount of heterogeneous data not specific to subtitles. Within the EBMT community there is a divided opinion about the effectiveness of using either heterogeneous or homogeneous source material (cf. Denoual, 2005). The output will be evaluated using both human evaluators and MT metrics. One commonly used MT metric we will employ is BLEU (Papineni et al., 2002), which is based on the idea of measuring the translation closeness between a candidate translation and a set of reference translations with a numerical metric. A BLEU score is given between 0 and 1, with a score of 1 indicating a perfect match between the output translation and (parts of) the reference translation(s). MT experts treat the notion of a “gold standard” translation as normal practice, comparing the system output against this reference translation. The benefit of comparing both sets of results will show the real need for human evaluation of MT output in the audio-visual domain. 6.3 Showcase We plan to showcase a working demo of our system at a conference in October of this year. This will allow people to see for themselves the quality of the EBMT subtitles and envisage ways in which the technology could be applied to the field of audio-visual translation. 11
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Proceedings - Translation Concepts

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