In this paper, a novel approach is proposed to automatically construct parallel discourse corpus for dialogue machine translation. Firstly, the parallel subtitle data and its corresponding monolingual movie script data are crawled and collected from Internet. Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts. We not only evaluate the mapping results, but also integrate speaker information into the translation. Experiments show our proposed method can achieve 81.79 and 98.64 accuracy on speaker and dialogue boundary annotation, and speaker-based language model adaptation can obtain around 0.5 BLEU points improvement in translation qualities. Finally, we publicly release around 100K parallel discourse data with manual speaker and dialogue boundary annotation.
There are two directions of work related to dialogue corpus construction. One is parallel corpora construction for dialogue or conversation MT @cite_21 @cite_10 @cite_12 @cite_17 @cite_6 @cite_3 @cite_24 . Thanks to the effects of crowdsourcing and fan translation in audiovisual translation @cite_22 , we can regard subtitles as parallel corpora. leveraged the existence of bilingual subtitles as a source of parallel data for the Chinese-English language pair to improve the MT systems in the movie domain. However, their work only considers sentence-level data instead of extracting more useful information for dialogues. Besides, Japanese researchers constructed a speech dialogue corpus for a machine interpretation system @cite_18 @cite_0 @cite_5 @cite_7 . They collected speech dialogue corpora for machine interpretation research via recording and transcribing Japanese English interpreters' consecutive simultaneous interpreting in the booth. The German VERBMOBIL speech-to-speech translation programme @cite_28 also collected and transcribed task-oriented dialogue data. This related work focused on speech-to-speech translation including three modules of automatic speech recognition (ASR), MT and text-to-speech(TTS).
@cite_18 @cite_22 @cite_7 @cite_28 @cite_21 @cite_6 @cite_3 @cite_24 @cite_0 @cite_5 @cite_10 @cite_12 @cite_17
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This paper proposes to use DTW to construct parallel corpora from difficult data. Parallel corpora are considered as raw material for machine translation (MT), frequently, MT systems use European or Canadian parliament corpora. In order to achieve a realistic machine translation system, we decided to use movie subtitles. These data could be considered difficult because they contain unfamiliar expressions, abbreviations, hesitations, words which do not exist in classical dictionaries (as vulgar words), etc. The obtained parallel corpora can constitute a rich ressource to train decoding spontaneous speech translation system. From 40 movies, we align 43013 English subtitles with 42306 French subtitles. This leads to 37625 aligned pairs with a precision of 92,3 . This paper presents a method for compiling a large-scale bilingual corpus from a database of movie subtitles. To create the corpus, we propose an algorithm based on Gale and Church’s sentence alignment algorithm(1993). However, our algorithm not only relies on character length information, but also uses subtitle-timing information, which is encoded in the subtitle files. Timing is highly correlated between subtitles in different versions (for the same movie), since subtitles that match should be displayed at the same time. However, the absolute time values can’t be used for alignment, since the timing is usually specified by frame numbers and not by real time, and converting it to real time values is not always possible, hence we use normalized subtitle duration instead. This results in a significant reduction in the alignment error rate. This paper describes a methodology for constructing aligned German-Chinese corpora from movie subtitles. The corpora will be used to train a special machine translation system with intention to automatically translate the subtitles between German and Chinese. Since the common length-based algorithm for alignment shows weakness on short spoken sentences, especially on those from different language families, this paper studies to use dynamic programming based on time-shift information in subtitles, and extends it with statistical lexical cues to align the subtitle. In our experiment with around 4,000 Chinese and German sentences, the proposed alignment approach yields 83.8 precision. Furthermore, it is unrelated to languages, and leads to a general method of parallel corpora building between different language families. This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project. Speech-to-speech translation has been an important research topic with the advance of technologies for speech processing and language processing. This paper describes a bilingual speech dialogue corpus which has been constructed for research on simultaneous machine interpretation at the Center for Integrated Acoustic Information Research (CIAIR), Nagoya University. The corpus has been implemented by collecting simulated cross-lingual conversations between English speech and Japanese speech through simultaneous interpretation, and by transcribing them manually with bilingual sentence alignment. In the year 2002, 216 spoken dialogues have been collected under a real environment, and transcribed into text files consisting of about 300,000 morphemes. In order to utilize the bilingual corpus effectively, every source utterance speech has been segmented into interpreting units according to its word-for-word translation and the word alignment of them. The interpreting unit means a linguistic chunk that could be interpreted separately and simultaneously. This paper has investigated linguistic characters of such the unit, and examined the feasibility of simultaneous machine interpretation. In this paper on-going work of creating an extensive multilingual parallel corpus of movie subtitles is presented. The corpus currently contains roughly 23,000 pairs of aligned subtitles covering about 2,700 movies in 29 languages. Subtitles mainly consist of transcribed speech, sometimes in a very condensed way. Insertions, deletions and paraphrases are very frequent which makes them a challenging data set to work with especially when applying automatic sentence alignment. Standard alignment approaches rely on translation consistency either in terms of length or term translations or a combination of both. In the paper, we show that these approaches are not applicable for subtitles and we propose a new alignment approach based on time overlaps specifically designed for subtitles. In our experiments we obtain a significant improvement of alignment accuracy compared to standard length-based Sentence alignment is an essential step in building a parallel corpus. In this paper a specialized approach for the alignment of movie subtitles based on time overlaps is introduced. It is used for creating an extensive multilingual parallel subtitle corpus currently containing about 21 million aligned sentence fragments in 29 languages. Our alignment approach yields significantly higher accuracies compared to standard length-based approaches on this data. Furthermore, we can show that simple heuristics for subtitle synchronization can be used to improve the alignment accuracy even further. This paper addresses the problem of synchronizing movie subtitles, which is necessary to improve alignment quality when building a parallel corpus out of translated subtitles. In particular, synchronization is done on the basis of aligned anchor points. Previous studies have shown that cognate filters are useful for the identification of such points. However, this restricts the approach to related languages with similar alphabets. Here, we propose a dictionary-based approach using automatic word alignment. We can show an improvement in alignment quality even for related languages compared to the cognate-based approach.