Noisy-parallel and comparable corpora filtering methodology for the extraction of bi-lingual equivalent data at sentence level
This work addresses the need for clean bilingual data to enhance Machine Translation and NLP tools, but it appears incremental as it builds on existing heuristics with minor additions like synonyms and semantic analysis.
The research tackled the problem of extracting high-quality bilingual sentence pairs from noisy corpora by proposing a language-independent filtering approach, which improved Machine Translation system scores.
Text alignment and text quality are critical to the accuracy of Machine Translation (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 them leverage synonyms and semantic and structural analysis of text as additional information. Minimization of data loss was ensured. An improvement in MT system score with text processed using the tool is discussed.