Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level

Abstract

Text alignment and text quality are critical to the accuracy of Machine Trans-lation (MT) systems, some NLP tools, and any other text processing tasks requiring bilingual data. This research proposes a language-independent bi-sentence filtering approach based on Polish (not a position-sensitive language) to English experiments. This cleaning approach was developed on the TED Talks corpus and also initially tested on the Wikipedia comparable corpus, but it can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence comparison. Some of the heuristics leverage synonyms as well as semantic and structural analysis of text as additio-nal information. Minimization of data loss has been? ensured. An improvement in MT system scores with text processed using this tool is discussed.