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Lilt: poor prediction in En-Ru pair
投稿者: Yakov Katsman
Yakov Katsman
Yakov Katsman  Identity Verified
米国
Local time: 18:26
2016に入会
英語 から ロシア語
Dec 31, 2016

Tried Lilt for En-Ru translation using large TM from previous work in Wordfast.
First Lilt suggested segment translation usually is similar to Google Translate or other tools.
But when i make even small change/edit/correction in beginning of phrase the rest of suggested phrase became really bad compare to initial Lilt suggestion.
The more you edit the phrase the worse the rest of phrase getting.
Is it only in En-Ru pair? How this predictive feature works in other pairs? D... See more
Tried Lilt for En-Ru translation using large TM from previous work in Wordfast.
First Lilt suggested segment translation usually is similar to Google Translate or other tools.
But when i make even small change/edit/correction in beginning of phrase the rest of suggested phrase became really bad compare to initial Lilt suggestion.
The more you edit the phrase the worse the rest of phrase getting.
Is it only in En-Ru pair? How this predictive feature works in other pairs? Does it improve the rest of suggestion while you editing the beginning?
Thanks for feedback
Yakov ▲ Collapse
 
Spence Green
Spence Green  Identity Verified
米国
Local time: 15:26
2014に入会
英語 から アラビア語
+ ...
Debugging translation quality Jan 3, 2017

Yakov:

I've looked briefly into your account and can see why you might be observing a problem. To preserve your privacy, and the privacy of your data, I will send you an email separately.

For everyone else, here's a hint: translation quality is best when your TM data is similar to the text that you're translating. Massive, heterogeneous TMs of the sort commonly curated by veteran translators are better divided into coherent subsections for the purposes of MT training. H... See more
Yakov:

I've looked briefly into your account and can see why you might be observing a problem. To preserve your privacy, and the privacy of your data, I will send you an email separately.

For everyone else, here's a hint: translation quality is best when your TM data is similar to the text that you're translating. Massive, heterogeneous TMs of the sort commonly curated by veteran translators are better divided into coherent subsections for the purposes of MT training. Here are some other tips:

https://lilt.com/kb/troubleshooting/training-lilt

Spence ▲ Collapse
 


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Lilt: poor prediction in En-Ru pair







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