Iconic Translation: The Neural Frontier by John Tinsley (Iconic Translation Machines)
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Machine translation using neural networks is becoming more mainstream and the market for language services is growing to $40 billion. The document discusses an ensemble architecture that combines the strengths of neural and traditional machine translation methods to produce the most appropriate translation for the task. It provides examples of how neural methods have improved translation of short sentences and terminology but still struggle with longer sentences, omitted phrases, and sentence structure.
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Iconic Translation: The Neural Frontier by John Tinsley (Iconic Translation Machines)
- 1. Machine Translation The Neural Frontier
- 2. “Machine translation is on a path to becoming a mainstream solution” Finally
- 7. Great! So what type of machine translation?
- 8. That’s where we come in… The Iconic Ensemble Architecture™ The most appropriate solution for the task at hand
- 9. The most appropriate solution for the task at hand The Iconic Ensemble Architecture™
- 10. Neural
- 12. Real-world languages, content Chinese to English patents, mature production engine, highly tuned. Doing it right Apples to apples comparison Access to same training data, test data, including all of the ugly parts. Effective qualitative evaluation No one-size-fits-all, so what MT good and what and where does it fall down?
- 13. Short Sentences All Sentences Iconic MT Iconic Neural MT Neural MT works – and it’s good! It is not a silver bullet + word order + agreement - omitting phrases + terminology + error free output - sentence structure
- 14. Enhancing the Ensemble Architecture…
- 15. …with Neural MT – the best of both worlds
Notes de l'éditeur
- Usage rates for MT have made steady gains in recent years it is near a tipping point: It is now on a path to become a mainstream solution for both enterprises and LSPs within the next three years.
- Not only that, it’s a growing opportunity if we look in the context of language services as a whole
- Right now, MT is a small portion of that. We can’t say exactly how much, but…
- What we can say is that it’s growing, and it’s only going to get bigger year after year… BUT not only is it growing within traditional language services, it’s creating it’s own market outside of that, in cases where translation and MT was previously viable
- Growing in key areas where previous there was not even an application because it wasn’t practical We’re definitely now at a TIPPING POINT!
- That’s all well and good, but which MT? NO ONE-SIZE-FITS-ALL
- We’ve developed an MT framework that allows us to combine all types of MT, and natural language processing approaches, to chose the most appropriate solution for the task at hand. We call this THE ENSEMBLE ARCHITECTURE
- It does an On-the-fly combination of different translation outputs, from different technology – SMT, RULES, LANGUAGE SPECIFIC LIKE SPANISH, PHARMA, etc. to ensure you get the best possible translation for a given language, content type or writing style All while HIDING THE COMPLEXITY from the end user
- One word that’s been conspicuous by it’s absence is “NEURAL”. Hot topic, controversial at times with a lot of hype and research but LITTLE PRACTICAL CONSIDERATION
- We’re changing that. We decided to collaborate with Adapt to build neural MT and compare to state of the art large scale production engines – not just in terms of BLEU scores, but translation quality, and practicality. Taking commercial state of the art and combining it with cutting edge academic research
- KEY DIFFERENCE IS WE’RE DOING IT RIGHT REAL WORLD NO SANITIZED DATA FIND OUT WHAT IT’S GOOD FOR
- THERE’S VALUE TO BE ADDED, HOW CAN WE HARNESS? We literally already have the perfect environment to allow NMT to be another string in the bow and let us use the most appropriate MT for the job WHETHER IT BE NEURAL FOR KOREAN, FOR CHAT TEXT, OR WHATEVER THE CASE MAY BE
- It’s not a one size fits all solution and who knows when it will be, but we have developed a framework that allows us to leverage it’s strength on a case by case basis to deliver the best possible translation for a given task. Overtime we fully expect the “brain to grow” and become the best MT on offer for various language pairs and content types, and when it is, WE”RE PERFECTLY POSITIONS FROM A TECHINCOLOGY AND EXPERTISE PERSPECTIVE to capitalise on this wave.