Benchmarking SMT performance for Farsi using the TEP++ Corpus
Passban, Peyman, Way, AndyORCID: 0000-0001-5736-5930 and Liu, QunORCID: 0000-0002-7000-1792
(2015)
Benchmarking SMT performance for Farsi using the TEP++ Corpus.
In: 18th Annual Conference of the European Association for Machine Translation, 11 - 13 May 2015., Antalya, Turkey.
Statistical machine translation (SMT) suffers from various problems which are exacerbated where training data is in short
supply. In this paper we address the data
sparsity problem in the Farsi (Persian) language and introduce a new parallel corpus, TEP++. Compared to previous results the new dataset is more efficient for
Farsi SMT engines and yields better output. In our experiments using TEP++ as
bilingual training data and BLEU as a metric, we achieved improvements of +11.17
(60%) and +7.76 (63.92%) in the Farsi–
English and English–Farsi directions, respectively. Furthermore we describe an
engine (SF2FF) to translate between formal and informal Farsi which in terms of
syntax and terminology can be seen as
different languages. The SF2FF engine
also works as an intelligent normalizer for
Farsi texts. To demonstrate its use, SF2FF
was used to clean the IWSLT–2013 dataset
to produce normalized data, which gave
improvements in translation quality over
FBK’s Farsi engine when used as training
data