A Sentence Meaning Based Alignment Method for Parallel Text Corpora Preparation
This work addresses the need for accurate bilingual data preparation for machine translation and NLP tools, though it appears incremental as it builds on existing alignment methods with specific heuristics.
The paper tackles the problem of sentence alignment for parallel text corpora by proposing a language-independent method based on Polish-English experiments, which reduces data loss and improves machine translation scores.
Text alignment is crucial to the accuracy of Machine Translation (MT) systems, some NLP tools or any other text processing tasks requiring bilingual data. This research proposes a language independent sentence alignment approach based on Polish (not position-sensitive language) to English experiments. This alignment approach was developed on the TED Talks corpus, but can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence recognition. Some of them value synonyms and semantic text structure analysis as a part of additional information. Minimization of data loss was ensured. The solution is compared to other sentence alignment implementations. Also an improvement in MT system score with text processed with described tool is shown.