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Home Page: https://microsoft.github.io/inmt/
License: MIT License
Interactive Neural Machine Translation tool
Home Page: https://microsoft.github.io/inmt/
License: MIT License
Currently, the translation engine configurations are ingrained in the code, decouple the configurations to a separate json file and include in the .gitignore
file.
Currently, we use a non-versioned copy of OpenNMT which becomes difficult to update when there is an update upstream. Figure out a way to update OpenNMT while keeping our wrappers intact.
Currently, the results from the API is in this form:
{
"result": "Today 's weather is beautiful\nToday is \nToday 's \nThis day \nThe weather \nToday , ",
"attn": [
1,
1,
1,
1
],
"partial": "",
"ppl": 3.230648083472593,
"avg": -1.172682762145996
}
While this works for our case, it would be great to restructure it:
Currently, recording of keystrokes get replaced every time the translator opens the translation interface.
TODOs:
This includes:
Look at the question of the last person, do not know who to find the new model download address.
Currently, the maximum number of suggestions in the drop-down box is 5. Can we add that as a parameter to the API?
this secret needs to be removed and rotated ASAP.
inmt/InteractiveTranslation/settings.py
Lines 23 to 24 in c8074bd
Currently, every time a translation is needed, the translation API is called and the request is processed for that source and partial input. Implementing Translation Memory would mean that we can use translation suggestions which were previously done by the translators.
Should the TM be user specific or global? It can probably be a combination of both - maybe batch and compare the individual TM and add that to global if it is commonly occurring.
Should the TM suggestion be shown with a different color explicitly to specify it comes from the TM whereas the others come from NMT?
Currently, the transliteration helping mechanism takes the english input and then retrieves the appropriate transliteration and shows to the user. However, there are interaction flaws with this:
Where can I get the parallel data to train this on?
Add the lints to Azure Pipelines.
Currently, we are performing beam search based on the inputs at the end. However, there should be a method to change inputs in between, so that the suggestion can be made based on the prefix and suffix.
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