Manipulating Input Data for Machine Translation
Liling Tan and Francis Bond
Universität des Saarlandes and Nanyang Technological University
alvations@gmail.com and bond@ieee.org

Introduction•Data quality/quantity affects MT easily•What happens if we try to remove, addorrearrange
input data?

Character based MT•Phrase-based SMT depends on tokens•Diff segmenters produce diff tokens•What if we use characters for MT? (Nakov and Tiedemann, 2012)

Input: ‘これらカテゴリーに含まれる要素数を検討した’Approaches•Context sensitive data selection•Paraphrase data extension•Lexicon addition•Character level MT (JP-ZH)

Context Sensitive Data Selection•Medical domain made up the majority of theASPEC corpus (~30%)•Build separate model for medical text
•Use generic model to decode the rest

Paraphrase Data Extension•Generate EN paraphrases with ERG and ACE(Flickinger, 2000)•Append EN paraphrases with original JP
EN Input: The particle sizes of the products decreased as the amount ofseed increased.

MeCab: [‘これら’, ‘カテゴリー’, ‘に’, ‘含ま’, ‘れる’, ‘要素’, ‘数’, ‘を’, ‘検討’, ‘し’, ‘た’]
Juman: [‘これ’, ‘ら’, ‘カテゴリー’, ‘に’, ‘含ま’, ‘れる’, ‘要素’, ‘数’, ‘を’, ‘検討’, ‘した’]
KyTea: [‘これ’, ‘ら’, ‘カテゴリー’, ‘に’, ‘含’, ‘ま’, ‘れ’, ‘る’, ‘要素’, ‘数’, ‘を’, ‘検討’, ‘し’, ‘た’]
Char: [‘こ’, ‘れ’, ‘ら’, ‘カ’, ‘テ’, ‘ゴ’, ‘リ’, ‘ー’, ‘に’, ‘含’, ‘ま’, ‘れ’, ‘る’, ‘要’, ‘素’, ‘数’, ‘を’, ‘検’, ‘討’,‘し’, ‘た’]Results (JP-EN)
JP-EN• Paraphrases and lexicon have minor improvement from baseline• Human evaluation shows implementation or config. errors
JP-ZH• Character based MT achieves pretty good results for JP-ZH
BLEU RIBES HumanNAIST
35.8 0.811 56.25
WEBLIO-EJ1 33.4 0.795 43.25
Organizer 32.1 0.760 42.50
Kyoto-U
31.7 0.771 38.00
31.4 0.771 27.50
SAS_MT
Paraphrase 28.7 0.703
28.6 0.703 3.75
Baseline
28.1 0.693
LexiconContext
27.1 0.697
Table 1: EN-JP Results

Paraphrase: As the amount of seed increased, the particle sizes of theproducts decreased.
JP Input: 種の量が増加する程，生成物の粒子寸法は減少した。

Lexicon Addition•Added JP-EN translation dictionary (JICST, 2004)

System Setup• Moses phrase-based SMT (Koehn et al. 2007)• GIZA++ IBM4 (Och and Ney, 2003)• Bi-directional lex reordering (Koehn et al. 2005)• KenLM 5grams (Heafield, 2011)• Kneser-Ney smoothing (Kneser and Ney, 1995)• MERT , truecasing
References• Francis Bond, Eric Nichols, Darren Scott Appling, and Michael Paul. 2008. Improving statistical machine translation by paraphrasing the training data. In IWSLT.
• Dan Flickinger. 2000. On building a more efficient grammar by exploiting types. Natural Language Engineering.
• Chris Callison-burch and Miles Osborne. 2006. Improved statistical machine translation usingparaphrases. In HLT/NAACL.
• Kenneth Heafield. 2011. KenLM: faster and smaller language model queries. In WMT.
• JICST, editor. 2004. JICST Japanese-English translation dictionaries. Japan Information Centerof Science and Technology.
• Reinhard Kneser and Hermann Ney. 1995. Improved backing-off for m-gram language mod-eling. In ICASSP-95.
• Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation.
In NAACL• Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola
Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al. 2007. Moses: Open
source toolkit for statistical machine translation. In ACL.
• Lucian Vlad Lita, Abraham Ittycheriah, Salim Roukos and Nanda Kambhatla. 2003. tRuEcasIng. In ACL.
• Yuval Marton, Chris Callison-Burch, and Philip Resnik. 2009. Improved statistical machinetranslation using monolingually-derived paraphrases. In EMNLP.
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BLEU RIBES Human
NAIST
23.82 0.7236 40.50
21.07 0.701 25.00
Kyoto-UToshiba
20.61 0.707 23.25
Organizer 20.36 0.683 25.50
19.86 0.706 22.50
EIWA
Lexicon18.91 0.646
Paraphrase 18.82 0.646 1.25
18.57 0.640
Baseline
Context
18.00 0.641
NII
17.47 0.630 -5.75
15.95 0.648 -17.00
TMU
Table 2: JP-EN Results

BLEU RIBES Human
NAIST
40.15 0.845 50.75
SAS_MT
37.07 0.833 22.50
ORGANIZER 36.64 0.825 16.00
Kyoto-U34.83 0.802 7.50
Character 34.64 0.784 -1.00
33.46 0.771
Baseline
EIWA
33.87 0.808 15.00
Table 3: ZH-JP ResultsConclusion

BLEU RIBES Human
NAIST
30.53 0.8296 17.75
ORGANIZER 28.65 0.8091 14.00
NICT
27.98 0.8060 6.50
Baseline
27.92 0.7938
Kyoto-U
27.67 0.7964 -8.75
27.42 0.8044 0.75
TOSHIBA
BJTUNLP
24.12 0.7948 -3.75
Character 23.09 0.7794 10.00
Table 4: JP-ZH Results• Data manipulation seems to affect BLEU score minimally• More experiments necessary to improve system before conclusive results

Acknowledgements
The research leading to these results has received funding from the People Programme(Marie Curie Actions) of the European Union's
Seventh Framework Programme FP7/20072013/ under REA grant agreement n ◦ 317471.