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- 1. On the Utility of Moses for Sinhala Tamil Translation 1 Yashothara.S, Dr.R.T.Uthayasanker National Language Processing center
- 2. Outline Background: Statistical Machine Translation (SMT) Introduction to Moses Training Decoder 2
- 3. Machine Translation Process of translating from one language into another language using a computer Types of machine translation Rule based Example based Knowledge based Statistical based Hybrid model based Neural network based 3 ComputerSource Target
- 4. Statistical Machine Translation 4 Hmmm. Every times she sees 犇伍犒, she either types 牀牀牀む or 牀牀牆牀牆牀牆牀 but if she sees 犇伍犒 犇園狂 she always types 牀牀牀む S S T S T T Translate, translate Parallel Corpus
- 5. Statistical Machine Translation 5 s-Sinhala t-Tamil TM LM P(t|s) P(s|t) p(t)
- 6. Statistical Machine Translation 6 Translation Model Language ModelTM LM Decoder 犇伍犒 犇犇 犇園狂 犇材蹡巌怯犇 犒 犇 . 牀牀牀む 牀牀萎 牀牀鉦巌牀牆牀牀鉦牀牆.
- 7. Moses Open source SMT framework Language independent Plug and play Steps 1. Preprocessing 2. Translation Model Building 3. Language Model Building 4. Decoding 7
- 8. Step1: Preprocessing Tokenization: Splitting the sentences as tokens tokenizer.perl script can be used. Example: Before tokenizing 犇犒犒犇 犒犇犇犒犇材 犇 犒犇死境犒犇死 犇準橋犒 犇犒犇園犇. 牀牀橿牀 牀む牀朽迦牀牀牀 牀朽巌牆牀牀朽牀牆. After Tokenizing 犇犒犒犇 犒犇犇犒 犇材 犇 犒犇死境犒犇死 犇準橋犒 犇犒犇 犇 . 牀牀橿牀 牀む牀朽 牆 牀牀牀 牀朽巌 牆 牀牀朽牀 牆 . 8
- 9. Step1: Preprocessing Cleaning: Removing low quality sentences clean-corpus-n.perl can be used. 9 Sinhala Tamil 犇犇 犒犇園犇犒 犒蹡 犇巌犇伍犇犒犇伍凶 犇犇園犒犒犇犒 犇犒犇犒犒犇材犒 犇犇犒犇 犒犒犇犒 犇園狂犒 犇 犇謹 犒犇園蹡 犇犒犇死犇犇伍怯犒 犇犇園境犒犇死犒 犒犒犇犒犒犒. 牀牀朽萎牀牆牀牆 牀牀鉦牀牀鉦牀迦牆牀牆牀牆 牀牀牆牀牀牀橿逗牆牀牀牆牀牀逗牀鉦 牀牀牆牀む萎牀牆牀牀む牀む逗迦 牀牀む牀牀園牀園 牀牀園牀む逗牆牀牀牆牀む牀む逗牀む牆 牀牀逗牆牀牀萎 牀牀牀鉦萎牀む牀む牀鉦 牀牀萎牀朽萎牀牆牀牆.. 牀牀牀む 牀牀牀牀萎 牀牆牀む. 犒犒犒犒 犒犒犒犇材蹡準矯犒犇 犇犇萎犇材蹡巌怯犇 犇犇犒犒犒 犇犒犒 犇犇萎犇材蹡巌怯犇材凶 犇巌犇死犒犒 犒犒犇伍凶 犇犇犇犒犇死犒犒 犒犒犇園犇 犇犇巌犒蹡 犒犇園 犇 . 牀牀牀萎牀牆牀む 犇伍犒 犇伍犇犒犇死犇材犒 犇園狂 犒犒犇謹 犇. 牀牀牀む 牀牀牆牀牀逗牀逗牆 牀牀牀牀萎 牀牆牀牀.
- 10. 10 Language Model Translation Model TM LM Decoder Parallel corpus 牀牀牀む 牀牀萎 牀牀鉦巌牀牆牀牀鉦牀牆.犇伍犒 犇犇 犇材蹡巌怯犇 犇犒.
- 11. Step 2:Building Translation Model Assigns probability P(s|t) to the pair of target and source words/phrases 11 Sinhala Tamil (s|t) 犇伍犒 牀牀牀む 0.66 犇伍犒 牀牀牆牀牆牀牆牀 0.22 犇伍犒 犇犇巌 牀牀牀む 牀牆牀む牀む牀牆 0.12 犇伍犒 犇園狂 犇犒 犒 牀牀牀む 牀牀牀牀萎 牀牆牀む 0.22 E.g. 犇伍犒 犇園狂 犇犒 犒 犇犒. 牀牀牀む 牀牀牀牀萎 牀牆牀む. 犇伍犒 犇犇巌 . 牀牀牆牀牆牀牆牀 牀牆牀む牀む牀牆. Word Alignment toolS T P(s|t) GIZA++
- 12. 12 Language Model Phrase Table LM Decoder Monolingual corpus Si Ta (s|t) 犇伍犒 牀牀牀む 0.66 犇伍犒 犇園狂 牀牀牀む 牀牀牀牀萎 0.12 犇伍犒 犇犇 犇材蹡巌怯犇 犇犒. 牀牀牀む 牀牀萎 牀牀鉦巌牀牆牀牀鉦牀牆.
- 13. Building Language model Used to ensure the fluent output. Getting probability of each word according to the n-grams. Standardly calculated with a trigram language model Using KenLM or SRILM* or irstlm E.g. 牀萎鉦牆 牀牀牆牀牀 牀牀牀逗む牀む鉦牆 牀萎鉦牆 牀牀牆牀牀 牀 牆牀牀逗牀鉦牆 13 Count(牀萎鉦牆 牀牀牆牀牀 牀牀牀逗む牀む鉦牆) Count(牀萎鉦牆 牀牀牆牀牀) P(牀牀牀逗む牀む鉦牆| 牀萎鉦牆 牀牀牆牀牀) = w3 w1w2 score 牀牀牀逗む牀む鉦牆 牀萎鉦牆 牀牀牆牀牀 -1.855783 牀 牆牀牀逗牀鉦牆 牀萎鉦牆 牀牀牆牀牀 -0.4191293
- 14. w3 w1w2 score 牀牀鉦牆牀牀逗 牀牀鉦牆 牀牀牆牀牆牀牆 -1.855783 牀牀牀 牀牀牀牆 牀牀牆牀牆牀牆 牀牀鉦牆牀牀逗 -0.4191293 14 Phrase Table Decoder Language Model Table Si Ta (S|T) 犇伍犒 牀牀牀む 0.66 犇伍犒 犇園狂 牀牀牀む 牀牀牀牀萎 0.12 牀牀牀む 牀牀萎 牀牀鉦巌牀牆牀牀鉦牀牆.犇伍犒 犇犇 犇材蹡巌怯犇 犇犒.
- 15. 15 牀牀牀む 牀牀牆牀牆牀牆牀 牀牀鉦巌牀牆牀牀鉦牀牆 牀牀萎 犇犇 犇材蹡巌怯犇 犇犒. Sinhala Tamil (s|t) 犇伍犒 牀牀牀む 0.66 犇伍犒 牀牀牆牀牆牀牆牀 0.22 犇犇 牀牀萎 0.34 犇伍犒 犇犇 牀牀牀む 牀牀萎 0.23 犇材蹡巌怯犇 牀牀鉦巌牀牆牀牀鉦牀牆 0.25 犇犒 牀牆牀む 0.12 犇材蹡巌怯犇 犇犒 牀牀鉦巌牀牆牀牀鉦牀牆 0.62 牀牆牀む鉦牀鉦巌牀牆牀牀鉦牀牆 牀牀萎 牀牀鉦巌牀牆牀牀鉦牀牆 牀牀鉦巌牀牆牀牀鉦牀牆 牀牀牀む 牀牀萎 牀牆牀む 牀牀鉦巌牀牆牀牀鉦牀牆 犇伍犒 牀牀鉦巌牀牆牀牀鉦牀牆 牀牆牀む
- 16. Using Moses for Si-Ta Translation Custom Tokenization Morphology rich languages Low resource languages Standards are not well established 16