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.
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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.

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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.

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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. 

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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?

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Blackout Redaction is Making Your Job Harder than it Needs to Be

How blackout redaction is making your job harder

When it comes to protecting classified data, blackout redaction has been in use for at least a century. While it is not the only acceptable form of data sanitization, it is historically the oldest and most commonly utilized by eDiscovery firms. This is despite the fact there are more modern and easy-to-use alternatives that save time and reduce errors. The two main data sanitization alternatives that meet legal requirements include anonymization and pseudonymization.

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The California Consumer Protection Act’s (CCPA) Impact on eDiscovery Firms

As noted by Anju Khurana, Head of Privacy of the Americas, Bank of New York Mellon, “There are now over 100+ privacy laws in the world and GDPR is driving other countries to adopt similar regulations.” (corpcounsel.com, Oct. 2019). The California Consumer Protection Act (“CCPA”) which comes into effect on January 1, 2020, is the latest, and very likely not the last. Most data privacy experts anticipate additional states enacting data privacy regulations and think it likely that Congress will eventually do so at the federal level.

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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.

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The art of speeding up NMT with SYSTRAN 2nd generation engines

Machine Translation users care about quality and performance. Based on our own observations and the feedback we’ve received; the quality of our Neural MT is impressive. Evaluating performance is a stickier subject, but we’d like to dig our hands in and present our innovations and achievements and how it benefits NMT users.

By performance we mostly mean the manner in which a system performs in terms of speed and efficiency in varying production environments. It is important to note that performance and quality in Neural MT are tightly connected: it is easy to accelerate a given model compromising on the quality. Therefore, when evaluating performance improvement, we always check that quality remains very close to optimal quality.

Since switching to NMT at the end of 2016, we’ve invested our R&D efforts into optimizing our engines to be more efficient, while maintaining and even improving translation accuracy. Our latest, 2nd generation NMT engines, available in our latest release of SYSTRAN Pure Neural® Server, implements several technical optimizations that make the translation faster and more efficient.

New model architecture

The first generation of neural translation engines was based on recurrent neural networks (RNN). This architecture requires the source text to be encoded sequentially, word by word, before generating the translation.

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Online Translation Tools and Data Breaches

Man is holding a tablet through which he has access to users data.

Is Your Team Violating Data Compliance Laws?

Data leakage and lack of information are two critical issues that can harm businesses. Nonetheless, due to the ever-growing global marketing and communication needs, the temptation to use the fast and free online translation tools are rising.

Apart from the apparent dangers that these tools pose to businesses such as miscommunication, loss of business, and cultural insults, there is critical important threat that many enterprises often fail to recognize. 

Whenever an employee uses a free online translation tool, they may cause massive data privacy breaches by making the consumer data searchable. Data breaches as such mainly happen due to employee negligence looking for quick machine translation, and it can often put millions of customers’ sensitive data at exposed on the internet.

Companies thus struggle to find the right balance between enabling business and securing information. Without the capability of translating software, potentially hundreds, if not thousands, of employees could turn to free translation tools to get their content translated in turn making the content available online.

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