Webinar Highlights: Transform Your Customer Care Team Into Language Ninjas

As part of our webinar series, one of our latest broadcast discussed and demonstrated the unique and innovative Language I/O + SYSTRAN solution, created in collaboration with our partner company Language I/O.

Hosted by J. Obakhan from SYSTRAN and Heather Shoemaker, CEO of Language I/O, the webinar discussed the power of integrating machine language translation technology into the customer care workflow.

Transform Your Customer Care Team Into Language Ninjas

Why Does the World Need Language I/O + SYSTRAN Solution?

Seventy-four percent of customers are more likely to buy a product or service if the company provides customer support in their native language.

Key Statistics Language I/O and SYSTRAN Solution

While this statistic highlights the importance of offering multilingual customer support,  building the staffing infrastructure to offer it comes with a slew of challenges — the greatest being the cost.

Without Language I/O + SYSTRAN solution, the global average annual salary for a fully-loaded support position is USD 45,000. This often results in an inefficient output when compared to a Neural Machine Translation (NMT) solution. An average support agent can only attend between 50 to 100 support requests a day.

The high cost and inefficiencies of investing in additional human language support, coupled with the low volume of support tickets/chats for a new language not warranting a full-time agent, highlights a scenario where an NMT solution would shine. For much less than the price of one language-specific CSR, NMT offers instantaneous support in 55+ languages all at once.

The Benefits of Using Language I/O + SYSTRAN Solution

Language I/O’s partnership with SYSTRAN makes it possible for a customer support agent to chat in real-time with their customer in many of the world’s widely-adopted languages. In case of support tickets rather than live chats, a hybrid of both human and machine support is available.

In addition, not having to hire native-speaking customer support agents saves companies a great deal, including many of Language I/O’s current customers — Wizards, Expedia, Shutterstock, LinkedIn, iRobot, and Betsson.

Other Benefits of the Language I/O + SYSTRAN Solution:

  • It supports all e-support channels ranging from tickets to chats, and articles in the CRM.
  • 72 percent of all current Language I/O + SYSTRAN customers have not had to hire additional support agents. Instead, the solution allowed their existing customer support agents to handle the volume of requests coming from all over the world without hiring additional staff.
  • It supports over 150 languages.
  • The direct SYSTRAN integration allows all MT content to pass through the SYSTRAN API without having to cut or paste.
  • Professional human translation services are available for machine translation post-editing (MTPE) and human translation (HT) in which professional linguists act as customer support agents.
  • It is GDPR compliant and encrypts personal data shared in ticket and chat content.
here

The ROI Calculator

Language I/O has also created an ROI Calculator that calculates the cost difference between hiring a staff of native-speaking customer support agents vs. your existing monolingual support agents coupled with SYSTRAN + Language I/O solution.

The calculator considers variables like the number of languages you wish to support, the number of days a week support is offered, cost per support agent, the location of your monolingual support team, number of chats/cases per year, average word per chat and case, etc. to estimate the amount you can save per year.

Watch the recording of the full webinar here.

Transform Your Customer Care Team Into Language Ninjas

For questions or more information on multilingual customer support, please email J. Obakhan at j.obakhan@systrangroup.com or set up a meeting here.

Webinar Highlights: Get More From SPNS9

Our webinar “Get More From SPNS9” on May 15th, 2020 was a huge success. The webinar demonstrated 6 new exciting upgrades to the SYSTRAN Pure Neural Server 9.6’s, further scaling its technological capabilities. Thank you to those who joined us.

In this post, we have compiled the highlights from the presentation and answers to the questions we receive after.

Continue reading

Specialized AI-Based Translation Technology 101

The minds behind SYSTRAN sit down for an interview regarding the complexities and the capacities of specialized neural machine translation engines.

Participants: Peter Zoldan, Senior Data Engineer -Software Engineer Linguistic Program, Svetlana Zyrianova, Linguistic Program, Petra Bayrami, Jr. Software Engineer – Linguistic Program, Natalia Segal, R&D Engineer.

How much data is required to create a specialized engine?

The more bilingual data, the better the quality. For broad domains such as news, millions of bilingual sentences will be required. However, if the domain is narrow, such as technical support documents for certain products, then even a small set of sentences of 50,000, noticeably improves the quality. 

The amount of data required will depend on how broad or narrow the demand you are specializing the engine into.

Continue reading

What’s So Special About Domain Specialization?

Student learning a second language

Language is messy. Ask any person who has ever had to learn a second language and they will tell you that the most difficult aspect isn’t learning all the rules, but understanding the exceptions to the rules — the real-world application of the language. 

Continue reading

NMT Scaling: 4 Ways to Create a Translation Powerhouse

e-Discovery can be a long, daunting process even in the best of times. In today’s globalized world of data, however, you not only have to worry about the sheer amount of information but also what language the content is in. This is where Neural Machine Translation comes in to break that language barrier. As fast as NMT is, though, odds are you have dreamed about how to make your systems even more efficient. How do you ensure any job can get completed on even the most ambitious of timelines?

Continue reading

OpenNMT-tf 2.0: a milestone in the OpenNMT project

OpenNMT-tf 2.0 workshop. Red Kakemonos and French Tech Central logo in front of the entrance door of Station F held in Paris in March 2018.
OpenNMT workshop held in March 2018 at Station F in Paris // Copyright SYSTRAN

SYSTRAN has been wholeheartedly involved in open source development over the past few years via the OpenNMT initiative,whose goal is to build a ready-to-use, fully inclusive, industry and research ready development framework for Neural Machine Translation (NMT). OpenNMT guarantees state-of-the-art systems to be integrated into SYSTRAN products and motivates us to continuously innovate.

In 2017, we published OpenNMT-tf, an open source toolkit for neural machine translation. This project is integrated into SYSTRAN’s model training architecture and plays a key role in the production of the 2nd generation of NMT engines.

Continue reading

Seven Tips for Better Translation Results

Whether you are using SYSTRAN’s Desktop, Enterprise Server, SaaS or online software, one question our IT Support is asked all the time is “How Can I Improve My Translation Output?” If incorrect or incomplete text or data is input into Machine Translation software, (also known as “garbage in, garbage out”) the outcome will, more often than not, also be incorrect or incomplete.

F.A.Q photo with tagline "How can i impove my translation output?" - SYSTRAN is showing ways to make better translation.

Here are seven tips to a better result:

  1. Use complete, grammatical sentences – Sentences should always start with a capital letter and end in either a period, exclamation point or question mark. A complete sentence always contains a verb, expresses an idea and makes sense standing alone.
  1. Avoid the passive voice – The passive voice is used to show interest in the person or object that experiences an action rather than the person or object that performs the action.
  1. Punctuation is important; clauses will translate best if separated by commas – Punctuation is the feature of writing that gives meaning to the written word. An error in punctuation can convey a completely different meaning to the one that is intended.
  1. Try to use simple, declarative sentences – A declarative sentence makes a statement, is in a present tense, and ends in a period. These are the most common sentences in the English language. It can either be a simple or compound sentence.
  1. Avoid ambiguity – To avoid ambiguity keep your sentences short, start with the subject, then the verb and end with the object. Use words and tenses consistently throughout.
  1. Avoid abbreviations, acronyms, jargon and colloquialisms – An abbreviation or acronym should first be spelled out if there are to be used consistently in a document. Colloquialisms are informal forms of speech and should be used mainly for speaking and not writing. Abbreviations, acronyms and jargon can be added to your User Dictionary or Translation Memories.
  1. Use your Dictionary Manager – SYSTRAN software includes a feature called the Dictionary Manager, which allows you to create your own dictionaries to supplement or override the main dictionary that comes with the program.  Using this feature can make substantial improvements to the translation.

The accuracy of the translation varies with the input.  If the input text is grammatically correct and unambiguous, it should translate well enough to convey the gist of what’s been written.

By: Ashley Shuler, Technical Support Analyst and Brooke Palm, Director of Customer Care SYSTRAN Software, Inc.

This was SYSTRAN Community Day’18!

Last week we hosted the 2018 edition of SYSTRAN Community Day! The conference was an exciting day full of energy, from Jean Senellart’s opening speech to our client success stories and celebrating SYSTRAN 50th anniversary! Here is a quick look at the conference highlights:

Jean Senellart announces the launch of a marketplace connecting the expertise of neural model trainers with the needs of industrial MT users

Full room for Jean Senellart conference at SYSTRAN Community Day, took place on November 8th at the Cloud Business Center in Paris

Jean Senellart, CEO of SYSTRAN France and CTO of the group opened the conference with a bold statement: the high quality of Neural Machine Translation has “commoditized” Machine Translation. As a commodity, NMT framework provides raw technology that needs to be refined, adapted and integrated for any industrial usage. After a look at the available NMT open source frameworks, including OpenNMT, cofounded and actively maintained by SYSTRAN, he made clear that streamlined training processes and data quality are the most crucial points to industrialize high quality neural machine translation.

Jean concluded his talk with the announcement of SYSTRAN marketplace, an open online platform where language experts have access to best of breed technology and framework to build, share, and sell language or domain models that can be accessed by industrial users. They will be able to select among hundreds of available models for any language pair and share feedback or evolution requests as per their specific needs.

Continue reading

SYSTRAN presents its latest translation engines: huge quality & speed improvement!

Logo of SYSTRAN Pure Neural Server technology, a huge gap in AI quality & speed improvement for translation

The latest version of our AI-powered Translation Software designed for Businesses

SYSTRAN Pure Neural® Server is our new generation of enterprise translation software based on Artificial Intelligence and Neural networks. It provides outstanding professional quality with the highest standards in data safety.

Our R&D team, extremely active to provide corporate users with state-of-the-art translation technology tailored for business, just released a new generation of Neural MT engines. SYSTRAN new engines are developed with OpenNMT-tf, our AI framework using latest TensorFlow features, and backed by a proprietary new training process: Infinite Training.

Continue reading

Open Source, Multilingual AI and Artificial Neural Networks : The new Holy Grail for the GAFA

Jean Senellart, CTO & CEO of SYSTRAN is explaining how SYSTRAN represent a GAFA alternative when they took benefit from Open Source, Multilingual AI and Artificial Neural Networks. Since 2016, there has been a sharp increase in open source machine translation projects based on neural networks or Neural Machine Translation (NMT) led by companies such as Google, Facebook and SYSTRAN. Why have machine translation and NMT-related innovations become the new Holy Grail for tech companies? And does the future of these companies rely on machine translation?

Never before has a technological field undergone so much disruption in such a short time. Invented in the 1960s, machine translation was first based on grammatical and syntactical rules until 2007. Statistical modelling (known as statistical translation or SMT), which matured particularly due to the abundance of data, then took over. Although statistical translation was introduced by IBM in the 1990s, it took 15 years for the technology to reach mass adoption. Neural Machine Translation on the other hand, only took two years to be widely adopted by the industry after being introduced by academia in 2014, showing the acceleration of innovation in this field. Machine translation is currently experiencing a golden age of technology.

From Big Data to Good Data

Not only have these successive waves of technology differed in their pace of development and adoption, but their key strengths or “core values” have also changed. In rule-based translation, value was brought by code and accumulated linguistic resources. For statistical models, the amount of data was paramount. The more data you had, the better the quality of your translation and your evaluation via the BLEU score (Bilingual Evaluation Understudy, the most widely used algorithm measuring machine translation quality). Now, the move to Machine translation based on neural networks and Deep Learning is well underway and has brought about major changes. The engines are trained to learn language as a child does, progressing step by step. The challenge is not only to process exponential data (Big Data) but more importantly to feed the engines the most qualitative data possible. Hence the interest in “Good data.”

Continue reading