1.
Searching for the Best
Translation Combination
Matīss Rikters, University of Latvia
The 7th International Conference
Human Language Technologies - the Baltic Perspective
Riga, Latvia
October 6, 2016

2.
Contents
Hybrid Machine Translation
Multi-System Hybrid MT
Simple combining of translations
– Combining full whole translations
– Combining translations of sentence chunks
Combining translations of linguistically motivated chunks
Searching for the best translation combination
Other work
Future plans

3.
Hybrid Machine Translation
Statistical rule generation
– Rules for RBMT systems are generated from training corpora
Multi-pass
– Process data through RBMT first, and then through SMT
Multi-System hybrid MT
– Multiple MT systems run in parallel

4.
Multi-System Hybrid MT
Related work:
SMT + RBMT (Ahsan and Kolachina, 2010)
Confusion Networks (Barrault, 2010)
– + Neural Network Model (Freitag et al., 2015)
SMT + EBMT + TM + NE (Santanu et al., 2014)
Recursive sentence decomposition (Mellebeek et al., 2006)

5.
Combining full whole translations
– Translate the full input sentence with multiple MT systems
– Choose the best translation as the output
Combining translations of sentence chunks
– Split the sentence into smaller chunks
• The chunks are the top level subtrees of the syntax tree of the sentence
– Translate each chunk with multiple MT systems
– Choose the best translated chunks and combine them
Combining Translations

6.
Choose the best candidate
KenLM (Heafield, 2011) calculates probabilities based on the observed
entry with longest matching history 𝑤𝑓
𝑛
:
𝑝 𝑤 𝑛 𝑤1
𝑛−1
= 𝑝 𝑤 𝑛 𝑤𝑓
𝑛−1
𝑖=1
𝑓−1
𝑏(𝑤𝑖
𝑛−1
)
where the probability 𝑝 𝑤 𝑛 𝑤𝑓
𝑛−1
and backoff penalties 𝑏(𝑤𝑖
𝑛−1
) are
given by an already-estimated language model. Perplexity is then
calculated using this probability: where given an
unknown probability distribution p and a proposed probability model q, it
is evaluated by determining how well it predicts a separate test sample
x1, x2... xN drawn from p.

7.
Whole translations
Choosing the best candidate:
A 5-gram language model trained with
– KenLM
– JRC-Acquis corpus v. 3.0 (Steinberger, 2006) - 1.4 million Latvian legal domain
sentences
– Sentences are scored with the query program that comes with KenLM
Test data
– 1581 random sentences from the JRC-Acquis corpus
– Tested with the ACCURAT balanced evaluation corpus - 512 general domain
sentences (Skadiņš et al., 2010), but the results were not as good

8.
Simple:
– Berkeley Parser (Petrov et al., 2006)
– Sentences are split into chunks from the top level subtrees
of the syntax tree
Linguistically motivated:
– Traverse the syntax tree bottom up, from right to left
– Add a word to the current chunk if
• The current chunk is not too long (sentence word count / 4)
• The word is non-alphabetic or only one symbol long
• The word begins with a genitive phrase («of »)
– Otherwise, initialize a new chunk with the word
– In case when chunking results in too many chunks, repeat the process, allowing
more (than sentence word count / 4) words in a chunk
Changes in the MT API systems:
– LetsMT API swapped with Hugo.lv API
– Added Yandex API
12-gram LM trained with
– DGT-Translation Memory corpus (Steinberger, 2011) – 3.1 million Latvian legal
domain sentences
Chunks

9.
Teikumu dalīšana tekstvienībās
Tulkošana ar tiešsaistes MT API
Google Translate Bing Translator LetsMT
Labākā tulkojuma izvēle
Tulkojuma izvade
Sentence tokenization
Translation with online MT
Selection of
the best translation
Output
Whole translations

10.
Teikumu dalīšana tekstvienībās
Tulkošana artiešsaistes MT API
Google
Translate
Bing
Translator
LetsMT
Labāko fragmentu izvēle
Tulkojumu izvade
Teikumu sadalīšana fragmentos
Sintaktiskā analīze
Teikumu apvienošana
Sentence tokenization
Translation with online MT
Selection of
the best chunks
Output
Syntactic analysis
Sentence chunking
Sentence
recomposition
Chunks

11.
Sentence chunking

12.
Simple chunks Linguistically motivated chunks
• Recently
• there
• has been an increased interest in the
automated discovery of equivalent
expressions in different languages
• .
• Recently there has been an increased interest
• in the automated discovery of equivalent
expressions
• in different languages .
Example sentence

13.
Example sentence
Recently there has been an increased interest
in the automated discovery
of equivalent expressions in different languages .

14.
Example sentence

15.
Whole translations
System BLEU
Hybrid selection
Google Bing LetsMT Equal
Google Translate 16.92 100 % - - -
Bing Translator 17.16 - 100 % - -
LetsMT 28.27 - - 100 % -
Hybrid Google + Bing 17.28 50.09 % 45.03 % - 4.88 %
Hybrid Google + LetsMT 22.89 46.17 % - 48.39 % 5.44 %
Hybrid LetsMT + Bing 22.83 - 45.35 % 49.84 % 4.81 %
Hybrid Google + Bing + LetsMT 21.08 28.93 % 34.31 % 33.98 % 2.78 %
May 2015 results (Rikters 2015)

16.
System
BLEU Hybrid selection
Whole
translations
Simple chunks Google Bing LetsMT
Google Translate 18.09 100% - -
Bing Translator 18.87 - 100% -
LetsMT 30.28 - - 100%
Simple Chunks G + B 18.73 21.27 74% 26% -
Simple Chunks G + L 24.50 26.24 25% - 75%
Simple Chunks L + B 24.66 26.63 - 24% 76%
Simple Chunks G + B + L 22.69 24.72 17% 18% 65%
September 2015 (Rikters and Skadiņa 2016(1))
Simple chunks

17.
System BLEU Equal Bing Google Hugo Yandex
BLEU - - 17.43 17.73 17.14 16.04
Whole translations G + B 17.70 7.25% 43.85% 48.90% - -
Whole translations G + B + H 17.63 3.55% 33.71% 30.76% 31.98% -
Simple Chunks G + B 17.95 4.11% 19.46% 76.43% - -
Simple Chunks G + B + H 17.30 3.88% 15.23% 19.48% 61.41% -
Linguistic Chunks G + B 18.29 22.75% 39.10% 38.15% - -
Linguistic Chunks G + B + H + Y 19.21 7.36% 30.01% 19.47% 32.25% 10.91%
Linguistically motivated chunks
January 2016 (Rikters and Skadiņa 2016(2))

18.
Searching for the best
The main differences:
• the manner of scoring chunks with the LM and selecting the best
translation
• utilisation of multi-threaded computing that allows to run the
process on all available CPU cores in parallel
• still very slow

19.
Searching for the best
Chunks Combinations
Count Percentage
Legal General Legal General
1 4 210 16 13.28% 3.13%
2 16 178 78 11.26% 15.23%
3 64 262 131 16.57% 25.59%
4 256 273 127 17.27% 24.80%
5 1024 275 94 17.39% 18.36%
6 4096 201 47 12.71% 9.18%
7 16384 96 11 6.07% 2.15%
8 65536 49 6 3.10% 1.17%
9 262144 37 2 2.34% 0.39%

20.
Searching for the best
Legal domain General domain

21.
Searching for the best
System
BLEU
Legal General
Full-search 23.61 14.40
Linguistic chunks 20.00 17.27
Bing 16.99 17.43
Google 16.19 17.72
Hugo 20.27 17.13
Yandex 19.75 16.03
May 2016

22.
Neural Network LM
16.00
17.00
18.00
19.00
20.00
21.00
22.00
23.00
24.00
25.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
0.11 0.20 0.32 0.41 0.50 0.61 0.70 0.79 0.88 1.00 1.09 1.20 1.29 1.40 1.47 1.56 1.67 1.74 1.77
BLEU
Perplexity
Epoch
Perplexity BLEU-HY Linear (BLEU-HY)

23.
Neural Network LM
13.30
13.80
14.30
14.80
15.30
15.80
16.30
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
0.11 0.20 0.32 0.41 0.50 0.61 0.70 0.79 0.88 1.00 1.09 1.20 1.29 1.40 1.47 1.56 1.67 1.74 1.77
BLEU
Perplexity
Epoch
Perplexity BLEU Linear (BLEU)

24.
More enhancements for the chunking step
Add special processing of multi-word expressions (MWEs)
Try out other types of LMs
– POS tag + lemma
– Recurrent Neural Network Language Model
(Mikolov et al., 2010)
– Continuous Space Language Model
(Schwenk et al., 2006)
– Character-Aware Neural Language Model
(Kim et al., 2015)
Choose the best translation candidate with MT quality estimation
– QuEst++ (Specia et al., 2015)
– SHEF-NN (Shah et al., 2015)
Handling MWEs in neural machine translation systems
Future work

25.
• Matīss Rikters
"Multi-system machine translation using online APIs for English-Latvian"
ACL-IJCNLP 2015
• Matīss Rikters and Inguna Skadiņa
"Syntax-based multi-system machine translation"
LREC 2016
• Matīss Rikters and Inguna Skadiņa
"Combining machine translated sentence chunks from multiple MT systems"
CICLing 2016
• Matīss Rikters
"K-translate – interactive multi-system machine translation"
Baltic DB&IS 2016
Related publications

26.
Code on GitHub
http://ej.uz/ChunkMT
http://ej.uz/SyMHyT
http://ej.uz/MSMT
http://ej.uz/chunker
Code on GitHub

27.
References• Ahsan, A., and P. Kolachina. "Coupling Statistical Machine Translation with Rule-based Transfer and Generation, AMTA-The Ninth Conference of
the Association for Machine Translation in the Americas." Denver, Colorado (2010).
• Barrault, Loïc. "MANY: Open source machine translation system combination." The Prague Bulletin of Mathematical Linguistics 93 (2010): 147-155.
• Heafield, Kenneth. "KenLM: Faster and smaller language model queries." Proceedings of the Sixth Workshop on Statistical Machine Translation.
Association for Computational Linguistics, 2011.
• Kim, Yoon, et al. "Character-aware neural language models." arXiv preprint arXiv:1508.06615 (2015).
• Mellebeek, Bart, et al. "Multi-engine machine translation by recursive sentence decomposition." (2006).
• Mikolov, Tomas, et al. "Recurrent neural network based language model." INTERSPEECH. Vol. 2. 2010.
• Petrov, Slav, et al. "Learning accurate, compact, and interpretable tree annotation." Proceedings of the 21st International Conference on
Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics,
2006.
• Raivis Skadiņš, Kārlis Goba, Valters Šics. 2010. Improving SMT for Baltic Languages with Factored Models. Proceedings of the Fourth International
Conference Baltic HLT 2010, Frontiers in Artificial Intelligence and Applications, Vol. 2192. , 125-132.
• Rikters, M., Skadiņa, I.: Syntax-based multi-system machine translation. LREC 2016. (2016)
• Rikters, M., Skadiņa, I.: Combining machine translated sentence chunks from multiple MT systems. CICLing 2016. (2016)
• Santanu, Pal, et al. "USAAR-DCU Hybrid Machine Translation System for ICON 2014" The Eleventh International Conference on Natural Language
Processing. , 2014.
• Schwenk, Holger, Daniel Dchelotte, and Jean-Luc Gauvain. "Continuous space language models for statistical machine translation." Proceedings of
the COLING/ACL on Main conference poster sessions. Association for Computational Linguistics, 2006.
• Shah, Kashif, et al. "SHEF-NN: Translation Quality Estimation with Neural Networks." Proceedings of the Tenth Workshop on Statistical Machine
Translation. 2015.
• Specia, Lucia, G. Paetzold, and Carolina Scarton. "Multi-level Translation Quality Prediction with QuEst++." 53rd Annual Meeting of the Association
for Computational Linguistics and Seventh International Joint Conference on Natural Language Processing of the Asian Federation of Natural
Language Processing: System Demonstrations. 2015.
• Steinberger, Ralf, et al. "Dgt-tm: A freely available translation memory in 22 languages." arXiv preprint arXiv:1309.5226 (2013).
• Steinberger, Ralf, et al. "The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages." arXiv preprint cs/0609058 (2006).
References

28.
Thank you!
Thank you!