ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications 12/06/2019
Editor's comments and enhancements are shown in green. [✓ Reviewed]
The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:
 Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
 Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
 Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
 Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
 Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
 Functional aspects, trustworthiness, and societal concerns
 AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.
Use Case Name:
AI solution to identify automatically false positives from a specific check for "untranslated target segments”"from an automated quality assurance tool
The scope of this use case is limited to automated linguistic quality assurance tools, but the outcome of this use case could be applicable to other areas, such as for example: Machine Translation, automated post-editing, Computer Aided Translation Analysis and pre-translation, etc. This use case will be relevant for contents across any domain.
To reduce the number of false positive issues for check for untranslated target segment for bilingual content with in-house automated quality assurance tool.
Short Description (up to 150 words)
In the future, we aim to build an AI solution that could automatically identify likely false positives issues from the results of the "check for untranslated target segments" following an approach where we could use machine learning based on already identified false positives by our users. The expected outcome would be to increase end user’s productivity when reviewing automated quality assurance findings and to change user behaviour to pay more attention to this type of issues by reducing the number of false positives in 80%. In addition, we would like to reduce the amount of time, we spent on a yearly basis on refining this check manually based on users' feedback.
Untranslated target segments contain characters, symbols, and words that remain the same in source and target language. These segments can contain, numbers, alphanumeric content, numbers, code, e-mail addresses, prices, proper nouns, etc. or any combination of those. On a yearly basis, this check produces over 1 Million potential issues across over 50 different languages. Refining this check manually based on annotated false positive data for each specific customer and product and for specific language pairs is very costly, and the coverage is never sufficient, as new content is constantly produced and there are always new opportunities for refining this check via code. In addition, because of the high proportion of false positives over (95.5%) our translators tend to ignore the output from this valuable check and in many cases, we suspect that valid relevant issues for situations when there are real forgotten translations are missed. There are typically three types of false positives for this type of check: 1) Language specific false positives, for example for situations where source and target segment need to be the same as the words from these segments are "cognates" with the same meaning. For example: Fig.1 2) Customer profile specific false positives, for example situations where certain segments are to be left untranslated based on specific guidelines from the customer, for example for segments that jut consist of Company names, Product Names or specific words and segments that have been determined as not to be translated by our customer: Fig.2 3) Segments that remain the same in source and target, because they act as special type of entities with some special meaning, for example: alphanumeric segments, for example part numbers, placeholders, code. Fig.3 The idea is to create an AI solution that can automatically identify results from the "check for untranslated target segment" that are likely to be a False Positive. With this solution, we expect to reduce the number of potential issues presented by this check to our end users in 80%. This way our end users can focus their efforts on those potential issues that are more likely to be valid corrections because there could have been a forgotten translation. In addition, we will be able to increase the productivity of our end users when reviewing automated quality assurance potential issues from their bilingual content evaluation, and we will be able to save costs internally as we won't have to manually implement code changes in this check based on manual analysis of our data based on user's annotation.
Challenges: Try to achieve eventually 80% of the accuracy of linguists when identifying false positives for untranslated target segments, preventing as much as possible false negatives. Issues: segmentation of false positive data by Customer and Product profile could be challenging.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
High quality company whitepapers and presentations
Publicly accessible sources with sufficient detail
This list is not exhaustive. Other credible sources may be acceptable as well.
Examples of credible sources:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013