Summary of the paper
| Title | An Iterative Approach for Mining Parallel Sentences in a Comparable Corpus |
|---|---|
| Authors | Lise Rebout and Phillippe Langlais |
| Abstract | We describe an approach for mining parallel sentences in a collection of documents in two languages. While several approaches have been proposed for doing so, our proposal differs in several respects. First, we use a document level classifier in order to focus on potentially fruitful document pairs, an understudied approach. We show that mining less, but more parallel documents can lead to better gains in machine translation. Second, we compare different strategies for post-processing the output of a classifier trained to recognize parallel sentences. Last, we report a simple bootstrapping experiment which shows that promising sentence pairs extracted in a first stage can help to mine new sentence pairs in a second stage. We applied our approach on the English-French Wikipedia. Gains of a statistical machine translation (SMT) engine are analyzed along different test sets. |
| Topics | Machine Translation, SpeechToSpeech Translation, Document Classification, Text categorisation |
| Full paper |
An Iterative Approach for Mining Parallel Sentences in a Comparable Corpus |
| Bibtex | @InProceedings{REBOUT14.43, author = {Lise Rebout and Phillippe Langlais}, title = {An Iterative Approach for Mining Parallel Sentences in a Comparable Corpus}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |