You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: articles/ai-services/translator/translator-pro/faq.yml
+4-4Lines changed: 4 additions & 4 deletions
Original file line number
Diff line number
Diff line change
@@ -95,12 +95,12 @@ sections:
95
95
- question: |
96
96
How does Microsoft Translator Pro ensure the quality and accuracy of translations in real-world, high-stakes environments?
97
97
answer: |
98
-
There are no universally recognized standard translation test sets for benchmarking. We refrain from publishing the absolute scores from our assessments of machine translation systems. Nevertheless, we continuously monitor quality and find that human evaluation yields the most significant and reliable results.
98
+
There are no universally recognized standard translation test sets for benchmarking and we refrain from publishing the absolute scores from our assessments of machine translation systems. Nevertheless, we continuously monitor quality and find that human evaluation yields the most significant and reliable results.
99
99
100
100
- question: |
101
101
What measures are in place to ensure that translations are accurate and reliable in real-world scenarios?
102
102
answer: |
103
-
"In addition to ongoing testing and evaluation, we also utilize speech and translation services trusted by customers for years in practical, real-world applications. For more information, *see* [Customer story: Berlitz empowered 500,000 language learners using Azure AI Speech](https://www.microsoft.com/customers/story/1650519804730300378-berlitz-language-azure-ai-speech-usa)."
103
+
In addition to ongoing testing and evaluation, we also utilize speech and translation services trusted by customers for years in practical, real-world applications. For more information, *see* [Customer story: Berlitz empowered 500,000 language learners using Azure AI Speech](https://www.microsoft.com/customers/story/1650519804730300378-berlitz-language-azure-ai-speech-usa).
104
104
105
105
- name: Ongoing Monitoring, Accountability, and Transparency
106
106
questions:
@@ -112,10 +112,10 @@ sections:
112
112
- question: |
113
113
What transparency measures are implemented to document and communicate model limitations, recurring issues, or risks associated with translations?
114
114
answer: |
115
-
Microsoft's Transparency Notes aim to clarify the workings of our AI technology, highlighting the choices available to system owners that can impact system performance and behavior. They emphasize the importance of considering the entire system, encompassing the technology, the people, and the environment. These notes can be a valuable resource when developing or deploying your own system. For more information, *see* [Microsoft Translator Pro Transparency Note]/legal/cognitive-services/translator/translator-pro-transparency-note#limitations).
115
+
Microsoft's Transparency Notes aim to clarify the workings of our AI technology, highlighting the choices available to system owners that can impact system performance and behavior. They emphasize the importance of considering the entire system, encompassing the technology, the people, and the environment. These notes can be a valuable resource when developing or deploying your own system. For more information, *see* [Microsoft Translator Pro Transparency Note](/legal/cognitive-services/translator/translator-pro-transparency-note#limitations).
116
116
117
117
- question: |
118
-
How is the quality differ when the app utilizes on-device models v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUME5BUDBVWUlaNDlUN0FRODRIQ082SjFVUCQlQCN0PWcu online models.
118
+
How does the quality differ when the app utilizes on-device models versus online models.
119
119
answer: |
120
120
The on-device models utilized by the Microsoft Translator Pro app for offline processing differ from the cloud models, leading to a variation in quality. The models implemented on the device are smaller compared to models used in the cloud.
0 commit comments