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Konstantin Savenkov
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MemoQ North American Summit May 2020
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This presentation covers our approach to building multi-purpose MT deployments. We talk about different enterprise use-cases for MT and the requirements of those use-cases. Since those requirements often have nothing to do with the objective linguistic quality, sometimes you don't want to select a specific MT engine just to meet them. Therefore, we provide some examples of how it's possible to fulfill those requirements by building NLP on top of your favorite Machine Translation black box.
Building Multi-Purpose MT Portfolio
Building Multi-Purpose MT Portfolio
Konstantin Savenkov
In this report, we have evaluated 6 modern domain-adaptive NMT engines on Biomedical dataset (English to German). ModernMT, Globalese, Google AutoML, IBM Custom NMT, Microsoft Custom Translate, and Tilde. We explored how they compare by performance (using reference-based scores, linguistic quality analysis and automatic quality estimation), total cost of ownership, dataset size requirements, training time, data protection policy and how to start using this advanced technology.
State of the Domain-Adaptive Machine Translation by Intento (November 2018)
State of the Domain-Adaptive Machine Translation by Intento (November 2018)
Konstantin Savenkov
We all want to act locally while going global, and maintain an inclusive multilingual work environment for the international workforce. Every AI model has its linguistic, cultural, and geopolitical biases. Besides providing better linguistic quality for specific languages and domains, a particular Machine Translation system may not be fully compliant with local dialect, tone of voice, gender, and data locality rules. In this talk, we consider practical cases when those biases create obstacles in building a global presence and an inclusive multilingual work environment for an international company. We discuss how to dodge those biases by using multi-vendor international AI, and in some cases go further, by leveraging those biases to create more diverse and inclusive translations.
Dodging AI biases in future-proof Machine Translation solutions
Dodging AI biases in future-proof Machine Translation solutions
Konstantin Savenkov
Evaluation of 19 major Cloud Machine Translation Engines (Alibaba, Amazon, Baidu, DeepL, Google, GRCom, IBM SMT and NMT, Microsoft SMT and NMT, ModernMT, PROMT, SAP, SDL Language Cloud, Systran SMT and PNMT, Tencent, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyse how the MT landscape changed over the last year.
State of the Machine Translation by Intento (July 2018)
State of the Machine Translation by Intento (July 2018)
Konstantin Savenkov
Talk by Konstantin Savenkov (Intento, Inc.) at Developer Week 2019 (Seattle: Cloud Edition). There are already hundreds of AI functions available via different APIs. Pick Machine Translation, Sentiment Analysis, Image Tagging or anything else - there's already a choice of 20-30 AI vendors to pick from. I will make a brief overview of what types of models are already available in the cloud, which of those enable customization and important things to look after when selecting the model (or a set of those) for a specific project. I will also touch AI API developer experience, to give an idea what a developer should be prepared for when choosing the API to work with. https://devweeksea2019.sched.com/event/OGSp/pro-talk-cloud-ai-landscape
Cloud Artificial Intelligence Landscape
Cloud Artificial Intelligence Landscape
Konstantin Savenkov
Evaluation of 25 major Cloud Machine Translation Services with Stock (pre-trained) models (Alibaba, Amazon, Baidu, CloutTranslate, DeepL, Google Translate, GTCom Yeecloud, IBM Watson v3, Microsoft Text Translator v3, ModernMT, Naver Papago, Niutrans, PROMT, SAP Translation Hub, SDL Language Cloud and BeGlobal, Systran SMT and PNMT, Sogou, Tencent, Tilde, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyze how the MT landscape changed over the last year.
State of the Machine Translation by Intento (stock engines, Jun 2019)
State of the Machine Translation by Intento (stock engines, Jun 2019)
Konstantin Savenkov
Evaluation of 14 major Cloud Machine Translation Engines (Google, Microsoft, IBM, IBM NMT, SAP, Amazon, Yandex, SDL, Systran, Systran PNMT, Baidu, GTCom, PROMT, DeepL) for 48 language pairs: performance, quality, language coverage, API update frequency.
State of the Machine Translation by Intento (March 2018)
State of the Machine Translation by Intento (March 2018)
Konstantin Savenkov
In this survey, we compare features, language support, and pricing for 15 vendors of Sentiment Analysis. We consider only hosted services with public API: several algorithms on Algorithmia marketplace, Microsoft Text Analytics, Repustate, Google Cloud Natural Language, IBM Watson NLU, Meaning Cloud, TheSay PreCeive, AWS Comprehend, Aylien, Bozon NLP, Salesforce Einstein Language, Twinword.
Cloud Sentiment Analysis - Vendor Overview (April 2018)
Cloud Sentiment Analysis - Vendor Overview (April 2018)
Konstantin Savenkov
Recommended
This presentation covers our approach to building multi-purpose MT deployments. We talk about different enterprise use-cases for MT and the requirements of those use-cases. Since those requirements often have nothing to do with the objective linguistic quality, sometimes you don't want to select a specific MT engine just to meet them. Therefore, we provide some examples of how it's possible to fulfill those requirements by building NLP on top of your favorite Machine Translation black box.
Building Multi-Purpose MT Portfolio
Building Multi-Purpose MT Portfolio
Konstantin Savenkov
In this report, we have evaluated 6 modern domain-adaptive NMT engines on Biomedical dataset (English to German). ModernMT, Globalese, Google AutoML, IBM Custom NMT, Microsoft Custom Translate, and Tilde. We explored how they compare by performance (using reference-based scores, linguistic quality analysis and automatic quality estimation), total cost of ownership, dataset size requirements, training time, data protection policy and how to start using this advanced technology.
State of the Domain-Adaptive Machine Translation by Intento (November 2018)
State of the Domain-Adaptive Machine Translation by Intento (November 2018)
Konstantin Savenkov
We all want to act locally while going global, and maintain an inclusive multilingual work environment for the international workforce. Every AI model has its linguistic, cultural, and geopolitical biases. Besides providing better linguistic quality for specific languages and domains, a particular Machine Translation system may not be fully compliant with local dialect, tone of voice, gender, and data locality rules. In this talk, we consider practical cases when those biases create obstacles in building a global presence and an inclusive multilingual work environment for an international company. We discuss how to dodge those biases by using multi-vendor international AI, and in some cases go further, by leveraging those biases to create more diverse and inclusive translations.
Dodging AI biases in future-proof Machine Translation solutions
Dodging AI biases in future-proof Machine Translation solutions
Konstantin Savenkov
Evaluation of 19 major Cloud Machine Translation Engines (Alibaba, Amazon, Baidu, DeepL, Google, GRCom, IBM SMT and NMT, Microsoft SMT and NMT, ModernMT, PROMT, SAP, SDL Language Cloud, Systran SMT and PNMT, Tencent, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyse how the MT landscape changed over the last year.
State of the Machine Translation by Intento (July 2018)
State of the Machine Translation by Intento (July 2018)
Konstantin Savenkov
Talk by Konstantin Savenkov (Intento, Inc.) at Developer Week 2019 (Seattle: Cloud Edition). There are already hundreds of AI functions available via different APIs. Pick Machine Translation, Sentiment Analysis, Image Tagging or anything else - there's already a choice of 20-30 AI vendors to pick from. I will make a brief overview of what types of models are already available in the cloud, which of those enable customization and important things to look after when selecting the model (or a set of those) for a specific project. I will also touch AI API developer experience, to give an idea what a developer should be prepared for when choosing the API to work with. https://devweeksea2019.sched.com/event/OGSp/pro-talk-cloud-ai-landscape
Cloud Artificial Intelligence Landscape
Cloud Artificial Intelligence Landscape
Konstantin Savenkov
Evaluation of 25 major Cloud Machine Translation Services with Stock (pre-trained) models (Alibaba, Amazon, Baidu, CloutTranslate, DeepL, Google Translate, GTCom Yeecloud, IBM Watson v3, Microsoft Text Translator v3, ModernMT, Naver Papago, Niutrans, PROMT, SAP Translation Hub, SDL Language Cloud and BeGlobal, Systran SMT and PNMT, Sogou, Tencent, Tilde, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyze how the MT landscape changed over the last year.
State of the Machine Translation by Intento (stock engines, Jun 2019)
State of the Machine Translation by Intento (stock engines, Jun 2019)
Konstantin Savenkov
Evaluation of 14 major Cloud Machine Translation Engines (Google, Microsoft, IBM, IBM NMT, SAP, Amazon, Yandex, SDL, Systran, Systran PNMT, Baidu, GTCom, PROMT, DeepL) for 48 language pairs: performance, quality, language coverage, API update frequency.
State of the Machine Translation by Intento (March 2018)
State of the Machine Translation by Intento (March 2018)
Konstantin Savenkov
In this survey, we compare features, language support, and pricing for 15 vendors of Sentiment Analysis. We consider only hosted services with public API: several algorithms on Algorithmia marketplace, Microsoft Text Analytics, Repustate, Google Cloud Natural Language, IBM Watson NLU, Meaning Cloud, TheSay PreCeive, AWS Comprehend, Aylien, Bozon NLP, Salesforce Einstein Language, Twinword.
Cloud Sentiment Analysis - Vendor Overview (April 2018)
Cloud Sentiment Analysis - Vendor Overview (April 2018)
Konstantin Savenkov
Evaluation of 23 major Cloud Machine Translation Services with Stock (pre-trained) models (Alibaba, Amazon, Baidu, DeepL, Google Translate, GTCom Yeecloud, IBM Watson v3, Microsoft Text Translator v3, ModernMT, Naver Papago, Niutrans, PROMT, SAP Translation Hub, SDL Language Cloud and BeGlobal, Systran SMT and PNMT, Sogou, Tencent, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyze how the MT landscape changed over the last year.
State of the Machine Translation by Intento (stock engines, Jan 2019)
State of the Machine Translation by Intento (stock engines, Jan 2019)
Konstantin Savenkov
Talk at Stanford HAI Workshop on "Measurement in AI Policy: Opportunities and Challenges", October 30, 2019, Stanford, USA When we procure Machine Translation vendors for the multi-vendor MT solutions we build for enterprises, we run a lot of MT evaluation projects. We evaluate commercial MT systems on public and private data to find the best system for a specific language pair and domain. These evaluations are quite different from what you see in WMT benchmarks, as we evaluate commercial systems, which are optimized for economic efficiency and real-time performance.
Progress in Commercial Machine Translation Systems
Progress in Commercial Machine Translation Systems
Konstantin Savenkov
We discuss the importance of evaluating pre-built and customizable MT engines towards different goals in Post-Edited Machine Translation (PEMT) and raw MT settings, as well as different approaches to those evaluations. We'll cover main pitfalls on the path to choose the right MT engine and possible workarounds. The primary focus is on reference-based assessment and how we run them at Intento. School of Advanced Technologies for Translators Friday 14 and Saturday 15 September 2018 - Milano (Italy) https://satt2018.fbk.eu/
EVALUATION IN USE: NAVIGATING THE MT ENGINE LANDSCAPE WITH THE INTENTO EVALUA...
EVALUATION IN USE: NAVIGATING THE MT ENGINE LANDSCAPE WITH THE INTENTO EVALUA...
Konstantin Savenkov
Training AI in-house is often infeasible as it requires a critical mass of talent and data, and has high R&D risks. For Cognitive AI, like machine translation and speech recognition, hundreds of pre-trained and adaptive models are already available on the market via APIs from many vendors. Their performance varies case by case and change often. Their prices are 100x-200x times different, hence a wrong choice may be a complete miss. In this talk, we argue that the only way to go is to evaluate and continuously optimize AI vendor portfolio and introduce our vendor-agnostic demand-side API platform for AI.
Improving the Demand Side of the AI Economy (API World 2018)
Improving the Demand Side of the AI Economy (API World 2018)
Konstantin Savenkov
Evaluation of 11 major Machine Translation (Google, Microsoft, IBM, SAP, Yandex, SDL, Systran, Baidu, GTCom, PROMT, DeepL) providers for 35 most popular language pairs: performance, quality, language coverage, API update frequency.
State of the Machine Translation by Intento (November 2017)
State of the Machine Translation by Intento (November 2017)
Konstantin Savenkov
Evaluation of nine major Machine Translation providers: performance, quality, language coverage, latency, API update frequency.
Intento Machine Translation Benchmark, July 2017
Intento Machine Translation Benchmark, July 2017
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Intento Enterprise MT Hub is a single place to evaluate, access and manage the enterprise MT Portfolio.
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Procure and deploy best-fit MT
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Insights on broad capabilities in Machine Translation optimisation, Localization, Global Enterprise Enabling with Intento. Procure and deploy best-fit MT
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Tony O’Dowd takes us through some of the most innovative technologies offered on the KantanMT.com platform which are helping a growing community of KantanMT users to develop and self-manage custom Machine Translation engines in the cloud. Maxim Khalilov then illustrates bmmt’s journey with Machine Translation on KantanMT. He discusses what they have achieved so far in terms of MT engine development and showcases the value that his team is bringing to their growing international client base through the use of Machine Translation.
New Breakthroughs in Machine Transation Technology
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Here is the first part of the presentation made by Synaptris at the ILUG 2008 Conference titled “The future of Notes & Domino reporting. Make your Notes data rock!” on June 4, 2008. This presentation takes you through the case study of Orange Romania, IntelliPRINT customer, and explains how they revolutionized the way they look at Lotus Notes & Domino data and achieved 80% savings in IT time, 15% reduction in overall IT overhead and RoI within 12 weeks of deploying IntelliPRINT Reporting. The other 3 parts of the Synaptris session at ILUG 08 will be soon uploaded here.
ILUG 2008 - The future of Notes & Domino Reporting - Let your Notes data rock...
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In this joint presentation, Tony O’Dowd, Founder and Chief Architect of KantanMT and Maxim Khalilov, Technical Lead of bmmt deliver an overview of the MT technology currently available in the language technology market, the challenges of operating MT systems at scale and speed, and their opinions on the future trajectory of MT. Each presentation will be grounded with client examples, and how they’ve successfully integrated MT into their localization workflows. Finally, both presenters will finish off with a 5 point checklist for successful MT deployment based on both the MT provider and LSP point of view. If you have any questions about this presentation or want to get in touch with either company please contact: Louise Irwin, Marketing Specialist at KantanMT (louisei@kantanmt.com) Peggy Linder, Operations Manager at bmmt (peggy.lindner@bmmt.eu)
5 challenges of scaling l10n workflows KantanMT/bmmt webinar
5 challenges of scaling l10n workflows KantanMT/bmmt webinar
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Smart print ® tracker
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State of the Machine Translation by Intento (stock engines, Jan 2019)
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Progress in Commercial Machine Translation Systems
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We discuss the importance of evaluating pre-built and customizable MT engines towards different goals in Post-Edited Machine Translation (PEMT) and raw MT settings, as well as different approaches to those evaluations. We'll cover main pitfalls on the path to choose the right MT engine and possible workarounds. The primary focus is on reference-based assessment and how we run them at Intento. School of Advanced Technologies for Translators Friday 14 and Saturday 15 September 2018 - Milano (Italy) https://satt2018.fbk.eu/
EVALUATION IN USE: NAVIGATING THE MT ENGINE LANDSCAPE WITH THE INTENTO EVALUA...
EVALUATION IN USE: NAVIGATING THE MT ENGINE LANDSCAPE WITH THE INTENTO EVALUA...
Konstantin Savenkov
Training AI in-house is often infeasible as it requires a critical mass of talent and data, and has high R&D risks. For Cognitive AI, like machine translation and speech recognition, hundreds of pre-trained and adaptive models are already available on the market via APIs from many vendors. Their performance varies case by case and change often. Their prices are 100x-200x times different, hence a wrong choice may be a complete miss. In this talk, we argue that the only way to go is to evaluate and continuously optimize AI vendor portfolio and introduce our vendor-agnostic demand-side API platform for AI.
Improving the Demand Side of the AI Economy (API World 2018)
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Konstantin Savenkov
Evaluation of 11 major Machine Translation (Google, Microsoft, IBM, SAP, Yandex, SDL, Systran, Baidu, GTCom, PROMT, DeepL) providers for 35 most popular language pairs: performance, quality, language coverage, API update frequency.
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New Breakthroughs in Machine Transation Technology
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Here is the first part of the presentation made by Synaptris at the ILUG 2008 Conference titled “The future of Notes & Domino reporting. Make your Notes data rock!” on June 4, 2008. This presentation takes you through the case study of Orange Romania, IntelliPRINT customer, and explains how they revolutionized the way they look at Lotus Notes & Domino data and achieved 80% savings in IT time, 15% reduction in overall IT overhead and RoI within 12 weeks of deploying IntelliPRINT Reporting. The other 3 parts of the Synaptris session at ILUG 08 will be soon uploaded here.
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In this joint presentation, Tony O’Dowd, Founder and Chief Architect of KantanMT and Maxim Khalilov, Technical Lead of bmmt deliver an overview of the MT technology currently available in the language technology market, the challenges of operating MT systems at scale and speed, and their opinions on the future trajectory of MT. Each presentation will be grounded with client examples, and how they’ve successfully integrated MT into their localization workflows. Finally, both presenters will finish off with a 5 point checklist for successful MT deployment based on both the MT provider and LSP point of view. If you have any questions about this presentation or want to get in touch with either company please contact: Louise Irwin, Marketing Specialist at KantanMT (louisei@kantanmt.com) Peggy Linder, Operations Manager at bmmt (peggy.lindner@bmmt.eu)
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Over the last year, we see increasingly more performant Text Transformers models, such as GPT-3 from OpenAI, Turing from Microsoft, and T5 from Google. They are capable of transforming the text in very creative and unexpected ways, like generating a summary of an article, explaining complex concepts in a simple language, or synthesizing realistic datasets for AI training. Unlike more traditional Machine Learning models, they do not require vast training datasets and can start based on just a few examples. In this talk, we will make a short overview of such models, share the first experimental results and ask questions about the future of the content creation process. Are those models ready for prime time? What will happen to the professional content creators? Will they be able to compete against such powerful models? Will we see GPT post-editing similar to MT post-editing? We will share some answers we have based on the extensive experimenting and the first production projects that employ this new technology.
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We have evaluated intent prediction performance, false positives, learning rate, language coverage, response time and pricing for 7 NLU providers: Amazon Lex, Facebook’s wit.ai, IBM Watson Conversation, Google’s API.ai, Microsoft LUIS, Recast.ai, Snips.ai
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The presentation describe what business goals may be driven by Recommender Systems, how to estimate the economic impact and determine when to start spending resources on RS.
The Economics of Recommender Systems
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Konstantin Savenkov
Overview talk on recommender systems from different perspectives. All math is out.
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Machine Translation Insights
1.
1 INTENTO MACHINE TRANSLATION INSIGHTS Konstantin Savenkov CEO Intento ©
Intento, Inc. / May 2020
2.
© Intento, Inc.
/ May 2020 ABOUT US 2 We are freshmen - 3 years in the industry — Tools to procure, utilise and maintain the best-fit AI. Mostly Machine Translation. — Several large enterprise clients (retail, travel, tech) — 40-70 languages each — Injecting MT into TMS, Customer Support, Website Translation, Communication, Software Development, Documentation and more — Procurement (evaluation) → deployment → improvement.
3.
© Intento, Inc.
/ May 2020 WORKING WITH MT SAME AS WITH SOFTWARE BUT DIFFERENT 3 PROCURE — DEPLOY — MAINTAIN train, evaluate and select integrate with software and workflows improve and update
4.
© Intento, Inc.
/ May 2020 PROCURING MT WHAT MAKES DIFFERENT MT DIFFERENT 4 Linguistic quality — Customizability — Tag support FOR A GIVEN MT SYSTEM, ALL THREE DEPEND ON A LANGUAGE PAIR AND DIRECTION
5.
Intento GENERIC STOCK MODELS Alibaba
Amazon Baidu DeepL eBay Google GTCom IBM Kakao Microsoft Mirai ModernMT Niutrans Naver Omniscien PROMT Rozetta SAP SDL Sogou Systran Tencent Tilde Yandex 5© Intento, Inc. / May 2020 VERTICAL STOCK MODELS CUSTOM TERMINOLOGY SUPPORT AUTO DOMAIN ADAPTATION MANUAL DOMAIN ADAPTATION Youdao Alibaba Baidu Cloud Translate Microsoft Omniscien PROMT SAP Systran Amazon Baidu Google IBM Microsoft Rozetta SDL Systran Yandex Globalese Google IBM Kantan Microsoft ModernMT Omniscien SDL Systran Alibaba Baidu Cloud Translate Iconic Omniscien PangeaMT Prompsit PROMT SDL Systran Tilde Yandex All product names, trademarks and registered trademarks are property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement. WAYS TO IMPROVE MT
6.
© Intento, Inc.
/ May 2020 PROCURING MT THINGS TO LOOK AFTER 6 Hard segments (for all MT) — Weak spots (for raw MT) — Typical segments (for PEMT) — One big model vs one model per TM — Real-world ROI, not proxy metrics weak spots hard segments typical MTAGREEMENT SENTENCE DIFFICULTY
7.
© Intento, Inc.
/ May 2020 DEPLOYING MT GETTING TO THE ROI 7 Receiving performance feedback — Optimizing MT vs Optimizing workflows — Business side: make sure better MT yields better ROI
8.
© Intento, Inc.
/ May 2020 MAINTAINING MT FOLLOW THE MOVING TARGET 8 Several update cycles: - quick changes (glossaries or per-sentence training) - regular re-training - re-evaluation — Monitoring the technology updates - terminology changes - fluency vs fidelity trade-offs
9.
THANKS! ks@inten.to 9 Konstantin Savenkov, CEO ks@inten.to 2150
Shattuck Ave Berkeley CA 94705 INTENTO https://inten.to
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