Automatic Parallel Fragment Extraction from Noisy Data

2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 538–542, Montr´eal, Canada, June 3-8, 2012. c⃝2012 Association for Computational Linguistics Automatic Parallel Fragment Extraction from Noisy Data Jason Riesa and Daniel Marcu Information Sciences Institute Viterbi School of Engineering University of Southern California {riesa, marcu}@isi.edu Abstract We present a novel method to detect parallel fragments within noisy parallel corpora. Isolat- ing these parallel fragments from the noisy data in which they are contained frees us from noisy alignments and stray links that can severely constrain translation-rule extraction. We do this with existing machinery, making use of an existing word alignment model for this task. We evaluate the quality and utility of the ex- tracted data on large-scale Chinese-English and Arabic-English translation tasks and show sig- nificant improvements over a state-of-the-art baseline. 1 Introduction A decade ago, Banko and Brill (2001) showed that scaling to very large corpora is game-changing for a variety of tasks. Methods that work well in a small- data setting often lose their luster when moving to large data. Conversely, other methods that seem to perform poorly in that same small-data setting, may perform markedly differently when trained on large data. Perhaps most importantly, Banko and Brill showed that there was no significant variation in per- formance among a variety of methods trained at- scale with large training data. The takeaway? If you desire to scale to large datasets, use a simple solution for your task, and throw in as much data as possible. The community at large has taken this message to heart, and in most cases it has been an effective way to increase performance. Today, for machine translation, more data than what we already have is getting harder and harder to come by; we require large parallel corpora to Figure 1: Example of a word alignment resulting from noisy parallel data. The structure of the resulting align- ment makes it difficult to find and extract parallel frag- ments via the standard heuristics or simply by inspection. How can we discover automatically those parallel frag- ments hidden within such data? train state-of-the-art statistical, data-driven models. Groups that depend on clearinghouses like LDC for their data increasingly find that there is less of a man- date to gather parallel corpora on the scale of what was produced in the last 5-10 years. Others, who di- rectly exploit the entire web to gather such data will necessarily run up against a wall after all that data has been collected. We need to learn how to do more with the data we already have. Previous work has focused on detecting parallel documents and sentences on the web, e.g. (Zhao and Vogel, 2002; Fung and Che- ung, 2004; Wu and Fung, 2005). Munteanu and Marcu (2006), and later Quirk et al. (2007), extend the state-of-the-art for this task to parallel fragments. In this paper, we present a novel method for de- tecting parallel fragments in large, existing and po- tentially noisy …

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