Difference between revisions of "User:Francis Tyers/Apertium 4"
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* [[apertium-neural]]: There should be a basic NMT implementation that functions in the Apertium ecosystem (C++,autotools,bash,apy,html-tools) for communities that want to build their own NMT systems and still take advantage of our ecosystem. We should be a one-stop shop for MT for marginalised langs. |
* [[apertium-neural]]: There should be a basic NMT implementation that functions in the Apertium ecosystem (C++,autotools,bash,apy,html-tools) for communities that want to build their own NMT systems and still take advantage of our ecosystem. We should be a one-stop shop for MT for marginalised langs. |
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== Data creation == |
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* Automatic multiword extraction using parallel corpora |
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* Recursive rule extraction |
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== End user == |
== End user == |
Revision as of 21:27, 21 June 2020
Wish list for Apertium 4:
Software engineering
- Make the different parts of the engine code more coherent in terms of modules
- Better test coverage and testing of existing (particularly newer) modules
Linguistic data
- Extract multiwords from lexicons into "separable" FSTs
- Train taggers for all languages using available corpora and a TLM
- At least one state-of-the-art language pair (wrt. Google).
Engine
- Better support for Unicode in lttoolbox.
- Use embeddings for morphological disambiguation and lexical selection
- Pass the surface form until transfer (to allow modules to look up surface form embeddings)
- Retire the HMM tagger
- Be able to train weights for morph analysis + morph. disambiguation + lexical selection + transfer end to end.
- e.g. can we treat the modules of the pipeline as a neural net and train the weights for them via backprop?
- Fully functional recursive transfer
- Per session state, this could be stored in something like a special blank that could be updated. It might contain things like domain, etc.
- Neural system
- apertium-neural: There should be a basic NMT implementation that functions in the Apertium ecosystem (C++,autotools,bash,apy,html-tools) for communities that want to build their own NMT systems and still take advantage of our ecosystem. We should be a one-stop shop for MT for marginalised langs.
Data creation
- Automatic multiword extraction using parallel corpora
- Recursive rule extraction
End user
- Format handling
- user-based chunking: Better treatment of "no-translate"
- User dictionaries
- Better handling of code-switching/mixed texts and informal text.