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Past, Present, and Future: Machine Translation & Natural Language Processing for Patent Information


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This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.

In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.

Published in: Technology
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Past, Present, and Future: Machine Translation & Natural Language Processing for Patent Information

  1. 1. ‘Past, Present, and Future’ Machine Translation & Natural Language Processing for Patent Information Dr. John Tinsley CEO, Iconic Translation Machines Ltd. EPOPIC. Madrid. 10th November 2016
  2. 2. BSc in Computational Linguistics PhD in Machine Translation Language Technology consultant Founder of Iconic Translation Machines Why listen to me? Machine Translation is what I do! The world’s first and only patent specific machine translation platform
  3. 3.  The use of computers to translate from one language into another  The use of computers to automate some, or all, of the translation process  An approach to Machine Translation, where translations for an input are estimated based on previous seen translation examples and associated (inferred) probabilities.  e.g. IPTranslator, Google Translate  Rule-based (or transfer-based): based on linguistic rules • e.g. Systran; Altavista’s Babelfish  Example-based: based on translation examples and inferred linguistic patterns Machine Translation: The Basics Machine Translation = automatic translation Statistical Machine Translation (SMT) Other approaches SMT is now by far the predominant approach*
  4. 4. A corpus (pl. corpora) is a collection of texts, in electronic format, in a single language  document(s)  book(s) Bilingual Corpora a bilingual corpus Note source language = original language or language we’re translating from target language = language we’re translating into A bilingual corpus is a collection of corresponding texts, in multiple languages  a document & its translation  a book in multiple languages  European Parliament proceedings
  5. 5. Aligned Bilingual Corpora A document-aligned bilingual corpus corresponds on a document level For translation, we required sentence-aligned bilingual corpora  The sentence on line 1 in the source language text corresponds to (i.e. is a translation of) the sentence on line 1 in the target language text etc.  Often referred to as parallel aligned corpora Sentence aligned bilingual parallel corpora are essential for statistical machine translation
  6. 6. Learning from Previous Translations Suppose we already know (from a sentence-aligned bilingual corpus) that:  “dog” is translated as “perro”  “I have a cat” is translated as “Tengo un gato” We can theoretically translate:  “I have a dog”  “Tengo un perro”  Even though we have never seen “I have a dog” before Statistical machine translation induces information about unseen input, based on previously known translations:  Primarily co-occurrence statistics  Takes contextual information into account
  7. 7. Statistical Machine Translation  Example of a small sentence-aligned bilingual corpus for English-French
  8. 8. Statistical Machine Translation  We take some new sentence to translate
  9. 9. Statistical Machine Translation  From the corpus we can infer possible target (French) translations for various source (English) words  We can then select the most probable translations based on simple frequencies (co-occurrence statistics)
  10. 10. Statistical Machine Translation Given a previously unseen input sentence, and our collated statistics, we can estimate translation
  11. 11. Advanced MT All modern approaches are based on building translations for complete sentences by putting together smaller pieces of translation Previous example is very simplistic  In reality SMT systems calculate much more complex statistical models over millions of sentence pairs for a pair of languages  Upwards of 2M sentence pairs on average for large-scale systems  Word-to-word translation probabilities  Phrase-to-phrase translation probabilities  Word order probabilities  Linguistic information (are the words nouns, verbs?)  Fluency of the final output Previous example is very simplistic Other statistics calculated include
  12. 12. Data is Key For SMT data is key  Information (word/phrase correspondences and associated statistics) is only based on what we have seen before in the data Important that data used to train SMT systems is:  Of sufficient size  avoid sparseness/skewed statistics  Representative and relevant  contains the right type of language  High-quality  absence of misspellings, incorrect alignments etc.  Proofed by human translators training data
  13. 13. Why is MT Difficult? A word or a phrase can have more than one meaning (ambiguity – lexical or structural)  e.g. “bank”, “dive”, “I saw the man with the telescope” People use language creatively  New words are cropping up all the time Linguistic differences between languages  e.g. structure of Irish sentences vs. structure of English sentences:  “Tá (Is) ocras (hunger) orm (on me)” <-> “I am hungry” There can be more than one way to express the same meaning.  “New York”, “The Big Apple”, “NYC”
  14. 14. Why is MT Difficult?  Israeli officials are responsible for airport security.  Israel is in charge of the security at this airport.  The security work for this airport is the responsibility of the Israel government.  Israeli side was in charge of the security of this airport.  Israel is responsible for the airport’s security.  Israel is responsible for safety work at this airport.  Israel presides over the security of the airport.  Israel took charge of the airport security.  The safety of this airport is taken charge of by Israel.  This airport’s security is the responsibility of the Israeli security officials.
  15. 15. No single solution for all languages Number agreement: the house / the houses vs. la maison / les maisons Gender agreement: the house / the cheese vs. la maison / le frommage English - Spanish English - French
  16. 16. No single solution for all languages English - German English - Chinese 种水果的农民 The farmer who grows fruit [Lit: “grow fruit (particle) farmer”]
  17. 17. Not all languages are created equal French German Turkish Finnish Spanish Chinese Korean Hungarian Portuguese Japanese Thai Basque
  18. 18. The Challenge of Patents L is an organic group selected from -CH2- (OCH2CH2)n-, -CO-NR'-, with R'=H or C1-C4 alkyl group; n=0-8; Y=F, CF3 … maximum stress of 1.2 to 3.5 N/mm<2> and a maximum elongation of 700 to 1,300% at 0[deg.] C. Long Sentences Technical constructions Largest single document: 249,322 words Longest Sentence: 1,417 words
  19. 19. The Challenge of Patents Very long sentences as standard Grammatically incomplete using nominal and telegraphic style (!) Passive forms are frequent Frequent use of subordinate clauses, participles, implicit constructs Inconsistent and incorrect spelling High use of neologisms Instances of synonymy and polysemy Spurious use of punctuation Authoring guide for “to be translated” text Patents break almost all of the rules!
  20. 20. Judge the quality of an MT system by comparing its output against a human-produced “reference” translation  Pros: Quick, cheap, consistent  Cons: Inflexible, cannot be used on ‘new’ input  Pros: Reliable, flexible, multi-faceted (fluency, error analyses, benchmarking)  Cons: Slow, expensive, subjective  Fluency vs. Adequacy Evaluating Machine Translation Quality Automatic Evaluation Human Evaluation Task-Based Evaluation
  21. 21. Evaluating Machine Translation Quality Task Based Evaluation  Standalone evaluation of MT systems is necessary to get a sense of the overall quality of a system  To determine the ultimate usability of an MT system, intrinsic task-based evaluation is required  Why? Fluency vs. Adequacy Fluency how fluent and grammatically correct the translation output is Adequacy how accurately the translation conveys the meaning of the source Output 1 The big blue house Output 2 The big house red Source La gran casa roja Task-Based Evaluation
  22. 22. Practical uses of Machine Translation Understand its limitations and you’ll understand its capabilities! No  Translate a patent for filing  Translate literature for publication  Translate marketing materials  Anything mission critical without review Yes  Productivity tool for professional translation  Understand foreign patents  Localisation processes and “controlled’ content  High volume, e.g. eDiscovery
  23. 23. Use cases in practice Product descriptions to open new markets MT for post-editing productivity across industries Developer, and user for web content Tens of thousands of people using online tools daily
  24. 24. Neural Networks  Using artificial intelligence and deep learning to develop a completely new way of doing machine translation! Quality Estimation  Functionality through which machine translation can “self- assess” the quality of the translations it produces. Online Adaptive Translation  Machine translations that can automatically learn and improve based on feedback, particularly from revisions. Use-case specific MT  Just like patent MT, but for countless other areas. Current Hot Topics
  25. 25. About Iconic We are a Machine Translation and Natural Language Processing software and services provider, delivering expert solutions with Subject Matter Expertise
  26. 26. Iconic Ensemble Architecture…
  27. 27. …enhanced with Neural MT
  28. 28. Speed, Cost, and Quality What is the difference between machine translation vs. manual translation when translating a 10 page patent document from Chinese into English? Machine Translation is not designed to replace professional translation but there are many cases where costly and time- consuming manual translation is simply not necessary.
  29. 29. - Data confidentiality - File formats - Potential for customisation, enhancements, and improvement for specific domains
  31. 31. Record Extraction Extraction algorithms work on cleaned OCR output, using patterns, keywords, and formatting information.
  32. 32. Citation Analysis Assessment of record and reference patterns Application for record extraction Tracking variations across years Application for bibliographic data fielding
  33. 33. Reference extraction + fielding
  34. 34. .com Visit and use the promo code epo2016 to get 20 free pages of translation
  35. 35. Thank You! @IconicTrans