Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction

International Joint Conference on Natural Language Processing, pages 286–292, Nagoya, Japan, 14-18 October 2013. Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction Haithem Afli, Lo¨ıc Barrault and Holger Schwenk Universit´e du Maine, Avenue Olivier Messiaen F-72085 - LE MANS, France FirstName.LastName@lium.univ-lemans.fr Abstract Discovering parallel data in comparable corpora is a promising approach for over- coming the lack of parallel texts in statis- tical machine translation and other NLP applications. In this paper we propose an alternative to comparable corpora of texts as resources for extracting parallel data: a multimodal comparable corpus of audio and texts. We present a novel method to detect parallel phrases from such corpora based on splitting comparable sentences into fragments, called phrases. The au- dio is transcribed by an automatic speech recognition system, split into fragments and translated with a baseline statistical machine translation system. We then use information retrieval in a large text corpus in the target language, split also into frag- ments, and extract parallel phrases. We compared our method with parallel sen- tences extraction techniques. We evaluate the quality of the extracted data on an En- glish to French translation task and show significant improvements over a state-of- the-art baseline. 1 Introduction The development of a statistical machine trans- lation (SMT) system requires one or more paral- lel corpora called bitexts for training the transla- tion model and monolingual data to build the tar- get language model. Unfortunately, parallel texts are a limited resource and they are often not avail- able for some specific domains and language pairs. That is why, recently, there has been a huge in- terest in the automatic creation of parallel data. Since comparable corpora exist in large quantities and are much more easily available (Munteanu and Marcu, 2005), the ability to exploit them is highly beneficial in order to overcome the lack of parallel data. The ability to detect these parallel data en- ables the automatic creation of large parallel cor- pora. Most of existing studies dealing with compara- ble corpora look for parallel data at the sentence level (Zhao and Vogel, 2002; Utiyama and Isa- hara, 2003; Munteanu and Marcu, 2005; Abdul- Rauf and Schwenk, 2011). However, the de- gree of parallelism can vary considerably, from noisy parallel texts, to quasi parallel texts (Fung and Cheung, 2004). Corpora from the last cate- gory contain none or few good parallel sentence pairs. However, there could have parallel phrases in comparable sentences that can prove to be help- ful for SMT (Munteanu and Marcu, 2006). As an example, consider Figure 1, which presents two news articles with their video from the English and French editions of the Euronews website1. The ar- ticles report on the same event with different sen- tences that contain some parallel translations at the phrase level. These two documents contain in par- ticular no exact sentence pairs, so techniques for extracting parallel sentences will not give good re- sults. We need a method to extract parallel phrases which exist at the …

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