Embracing Diversity: Searching over multiple languages

Tommaso Teofili
Suneel Marthi

June 12, 2017
Berlin Buzzwords, Berlin, Germany

$WhoAreWe

Tommaso Teofili
@tteofili

• Software Engineer, Adobe Systems
• Member of Apache Software Foundation,
  
• PMC Chair, Apache Lucene
• Committer and PMC on Apache Joshua, Apache OpenNLP, Apache JackRabbit

Suneel Marthi
@suneelmarthi

• Principal Software Engineer, Office of Technology, Red Hat
• Member of Apache Software Foundation
• Committer and PMC on Apache Mahout, Apache OpenNLP, Apache Streams

Agenda

• What is Multi-Lingual Search ?
• Why Multi-Lingual Search ?
• What is Statistical Machine Translation ?
• Overview of Apache Joshua
• Dataflow Pipeline
• Demo

What is Multi-Lingual Search ?

• Searching
• over content written in different languages
• with users speaking different languages
• both
• Parallel corpora
• Translating queries
• Translating documents

Why Multi-Lingual Search ?

Embracing diversity

• Most online tech content is in English
• Wikipedia dumps:
• en: 62GB
• de: 17GB
• it:   10GB
• Good number of non-English speaking users
• A lot of search queries are composed in English
• Preferable to retrieve search results in native language
• … or even to consolidate all results in one language

UC1 — tech domain, native first

UC2 — native only ?

What is Machine Translation ?

Generate Translations from Statistical Models trained on Bilingual Corpora.

Translation happens per a probability distribution p(e/f)

E = string in the target language (English)

F = string in the source language (Spanish)

e~ = argmax  p(e/f) = argmax p(f/e) * p(e)

e~ = best translation, the one with highest probability

Word-based Translation

How to translate a word → lookup in dictionary

Ge­bäu­de — building, house, tower.

Multiple translations

some more frequent than others
for instance: house and building most common

Look at a parallel corpus
(German text along with English translation)

Alignment

• In a parallel text (or when we translate), we align words in one language with the word in the other
  

  Das	Gebäude	ist	hoch
  ↓	↓	↓	↓
  the	building	is	high

• Word positions are numbered 1—4

Alignment Function

• Define the Alignment with an Alignment Function
• Mapping an English target word at position i to a German source word at position j with a function a : i → j
• Example

a : {1 → 1, 2 → 2, 3 → 3, 4 → 4}

One-to-Many Translation

A source word could translate into multiple target words

Phrase-based Translation

Alignment Function

• Word-Based Models translate words as atomic units
• Phrase-Based Models translate phrases as atomic units
• Advantages:
• many-to-many translation can handle non-compositional phrases
• use of local context in translation
• the more data, the longer phrases can be learned
• “Standard Model”, used by Google Translate and others

Phrase-Based Model

• Foreign input is segmented in phrases
• Each phrase is translated into English
• Phrases are reordered

Decoding

• We have a mathematical model for translation p(e|f)
• Task of decoding: find the translation ebest with highest probability
  

  ebest = argmax p(e|f)

• Two types of error
• the most probable translation is bad → fix the model
• search does not find the most probable translation → fix the search

Translation Process

Translate this query from German into English

Pick and input phrase, translate

Translation Process

Translate this query from German into English

er				ja noch nichts
↓
he		does not yet

Pick and input phrase, translate

Translation Process

Translate this query from German into English

er		trinkt		ja noch nichts
↓
he		does not yet		drink

Pick and input phrase, translate

Apache Joshua

• Statistical Machine Translation Decoder for phrase-based and hierarchical machine translation
• Written in Java
• Provide 64 language packs for machine translation
• https://cwiki.apache.org/confluence/display/JOSHUA/Language+Packs
• Project initiated by Johns Hopkins Univ. and University of Pennsylvania
• Presently incubating at Apache Software Foundation
• Used extensively by Amazon.com, NASA JPL
• https://cwiki.apache.org/confluence/display/JOSHUA
• @ApacheJoshua

Flows

References

• Apache Joshua — https://cwiki.apache.org/confluence/display/JOSHUA
• Apache OpenNLP — https://opennlp.apache.org
• GitHub — https://github.com/smarthi/BBuzz-multilang-search
• Slides — https://smarthi.github.io/bbuzz17-embracing-diversity-searching-over-multiple-languages/#/

Credits

• Joern Kottmann — PMC Chair, Apache OpenNLP
• Matt Post — PMC Chair, Apache Joshua
• Bruno P. Kinoshita — Committer on Apache OpenNLP, committer and PMC on Apache Commons and Apache Jena

Questions ???