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News stories discuss terrorism and new commercial ventures in almost exactly the same tone. As Pete Smith points out in the webinar link below, in business communication, and customer service and support scenarios the tone really matters. Enterprises that can identify dissatisfied customers and address the issues that cause dissatisfaction are likely to be more successful.

CX is all about tone and emotion in addition to the basic literal translation. Many users consider only the results of comparative evaluations — often performed by means of questionable protocols and processes using test data that is invisible or not properly defined — to select which MT systems to adopt. English to French.

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English to Chinese. English to Dutch. Vendor A — Vendor C — Vendor B — While this approach looks logical at one level, it often introduces errors and undermines efficiency because of the administrative inconsistency between different MT systems. Also, the suitability of the MT output for post editing may be a key requirement for localization use cases, but this may be much less important in other enterprise use cases.

The first post in this blog series exposes many of the fallacies of automated metrics that use string-matching algorithms like BLEU and Lepor , which are not reliable quality assessment techniques as they only reflect the calculated precision and recall characteristics of text matches in a single test set, on material that is usually unrelated to the enterprise domain of interest. The issues discussed challenge the notion that single-point scores can really tell you enough about long-term MT quality implications.

This is especially true as we move away from the localization use case. Speed, overall agility and responsiveness and integration into customer experience related data flow matters much more in the following use cases. The actual translation quality variance measured by BLEU and Lepor may have little to no impact on what really matters in the following use cases.

To effectively link MT output to business value implications, we need to understand that although linguistic precision is an important factor, it often has a lower priority in high-value business use cases. This view will hopefully take hold as the purpose and use of MT is better understood in the context of a larger business impact scenario, beyond localization. But what would more dynamic and informed approaches look like? MT evaluation certainly cannot be static since systems must evolve as requirements change.

Instead of a single-point score, we need a more complex framework that provides an easy, single measure that tells us everything we need to know about an MT system. Today, this is unfortunately not yet feasible. This is also true for automated metrics, which means that scores based on news domain tests should be viewed with care since they are not likely to be representative of performance on specialized enterprise content.

When rating different MT systems, it is essential to score key requirements for enterprise use, including:. Adaptability: Range of options and controls available to tune the MT system performance for very specific use cases. For example, optimization techniques applied to eCommerce catalog content should be very different from those applied to technical support chatbot content or multilingual corporate email systems. Data privacy and security: If an MT system will be used to translate confidential emails, business strategy and tactics documents, human evaluation requirements will differ greatly from a system that only focuses on product documentation.

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Some systems will harvest data for machine learning purposes, and it is important to understand this upfront. Deployment flexibility: Some MT systems need to be deployed on-premises to meet legal requirements, such as is the case in litigation scenarios or when handling high-security data. Expert services: Having highly qualified experts to assist in the MT system tuning and customization can be critical for certain customers to develop ideal systems.

IT integration: Increasingly, MT systems are embedded in larger business workflows to enable greater multilingual capabilities, for example, in communication and collaboration software infrastructures like email, chat and CMS systems. Overall flexibility: Together, all these elements provide flexibility to tune the MT technology to specific use cases and develop successful solutions. Ultimately, the most meaningful measures of MT success are directly linked to business outcomes and use cases.


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The definition of success varies by the use case, but most often, linguistic accuracy as an expression of translation quality is secondary to other measures of success. Linguistic quality matters but is not the ultimate driver of successful business outcomes.


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In fact, there are reports of improvements in output quality in an eCommerce use case that actually reduced the conversion rates on the post-edited sections, as this post-edited content was viewed as being potentially advertising-driven and thus less authentic and trustworthy. Global enterprise communication and collaboration. A more detailed presentation and webinar that goes into much more detail on this subject is available from Brightalk. In upcoming posts in this series, we will continue to explore the issue of MT quality assessment from a broad enterprise needs perspective.

More informed practices will result in better outcomes and significantly improved MT deployments that leverage the core business mission to solve high-volume multilingual challenges more effectively. Posted by Kirti Vashee at AM 0 comments. This is the first in a series of posts discussing various aspects of MT quality from the context of enterprise use and value, where linguistic quality is important, but not the only determinant of suitability in a structured MT technology evaluation process. A cleaner, more polished, and shorter studio version of this post is available here.

You can consider this post a first draft, or the live stage performance stream of consciousness version. As the use of enterprise machine translation expands, it becomes increasingly more important for users and practitioners to understand MT quality issues in a relevant, meaningful, and accurate way. The BLEU score is a string-matching algorithm that provides basic output quality metrics for MT researchers and developers.

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While it is widely understood that the BLEU metric has many flaws, it continues to be a primary metric used to measure MT system output even today, in the heady days of Neural MT. What has happened over the years is that people choose to interpret this as a measure of the overall quality of an MT system. BLEU scores only reflect how a system performs on the specific set of test sentences used in the test.

As there can be many correct translations, and most BLEU tests rely on test sets with only one correct translation reference, it means that it is often possible to score perfectly good translations poorly. It is very easy to use and interpret BLEU incorrectly and the localization industry abounds with examples of incorrect, erroneous, and even deceptive use. Scores are calculated for individual MT translated segments—generally sentences—by comparing them with a set of good quality human reference translations.

Most would consider BLEU scores more accurate at a corpus level rather than at a sentence level. BLEU together with human assessment remains the preferred metrics of choice today. BLEU is actually nothing more than a method to measure the similarity between two text strings. MT is a particularly difficult AI challenge because computers prefer binary outcomes, and translation has rarely if ever only one single correct outcome. T he most common way to measure quality is to compare the output strings of automated translation to a human translation text string of the same sentence.

The BLEU metric scores a translation on a scale of 0 to 1. The metric attempts to measure adequacy and fluency in a similar way to a human would, e. The closer to 1, the more overlap there is with a human reference translation and thus the better the system is. In a nutshell, the BLEU metric measures how many words overlap, giving higher scores to sequential words.

It is very unlikely that you would ever score 1 as that would mean that the compared output is exactly the same as the reference output. However, it is also possible that an accurate translation would receive a low score because it uses different words than the reference used. This problem potential can be seen in the following example. If we select one of these translations for our reference set, all the other correct translations will score lower!

To conduct a BLEU measurement the following data is necessary:. This is somewhat arbitrary. Random string matching scores should not be equated to overall translation quality. Therefore, although humans are the true test of correctness, they do not provide an objective and consistent measurement for any meaningful notion of quality. As would be expected using multiple human reference tests will always result in higher scores as the MT output has more human variations to match against. The NIST evaluation also defined the development, test, and evaluation process much more carefully and competently, and thus comparing MT systems under their rigor and purview was meaningful.

This has not been true for many of the comparisons done since, and many recent comparisons are deeply flawed. Automated quality measurement metrics have always been important to the developers and researchers of data-driven based MT technology, because of the iterative nature of MT system development, and the need for frequent assessments during the development of the system.

They can provide rapid feedback on the effectiveness of continuously evolving research and development strategies. Also, such a score does not incorporate the importance of overall business requirements in an enterprise use scenario where other workflow, integration, and process-related factors may actually be much more important than small differences in scores. Useful MT quality in the enterprise use context will vary greatly, depending on the needs of the specific use-case. You can learn a lot from voyaging through a filled journal. You can appreciate the work. You can see improvements.

You can be inspired by an idea and set off to build upon it in your next journal. My dream library would have loads of books and equal amounts of blank books. Blank books are symbols of what each of us is capable of creating, contributing, and sharing. It symbolizes that each of us has an important story to tell. The blank book symbolizes that the best chapters are unwritten and just waiting to unfold.

The computer, you say? The blank pages of a creativity program? Are they as wonderful as a blank paper journal? While the experience is not as sensual, the effect is the same ultimately. I am a big advocate of any program that has a blank screen as its main screen. Blank screens, like blank journals, beg to be adorned, to be added to, to have life breathed into them. Peter Reynolds peterhreynolds is an accomplished writer, storyteller, and illustrator.

While Peter H. Reynolds is indeed an artist and author, he would rather be known for his mission to use media to tell stories that matter and challenge us to reach our full potential. Peter also founded the Emmy award-winning children's multimedia company, FableVision , with his twin brother Paul. New approaches to building literacy through creative technology in elementary schools.

Four ways to engage digital learners. Get students ready for project-based learning. Lesson: Animal Riddles. Creative personalized learning: combining voice and choice. A curated, copyright-friendly image library that is safe and free for education. Digital Storytelling. Project-based Learning. Teaching and Learning. English Language Aquisition. Language Arts. Social Studies.

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Professional Learning. Topics Lessons Services. By Subject. Building Literacy in Elementary Students New approaches to building literacy through creative technology in elementary schools.

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