English-French Document Alignment Based on Keywords and Statistical Translation
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Proceedings |
| Conference | Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | bilingual document alignment |
| Description | In this paper we present our approach to the Bilingual Document Alignment Task (WMT16), where the main goal was to reach the best recall on extracting aligned pages within the provided data. Our approach consists of tree main parts: data preprocessing, keyword extraction and text pairs scoring based on keyword matching. For text preprocessing we use the TreeTagger pipeline that contains the Unitok tool (Michelfeit et al., 2014) for tokenization and the TreeTagger morphological analyzer (Schmid, 1994). After keywords extraction from the texts according TF-IDF scoring our system searches for comparable English-French pairs. Using a statistical dictionary created from a large English-French parallel corpus, the system is able to find comaparable documents. At the end this procedure is combined with the baseline algorithm and best one-to-one pairing is selected. The result reaches 91.6% recall on provided training data. After a deep error analysis (see section 5) the recall reached 97.4%. |
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