Some of OpenNMT users have mentioned using seq2seq models for tagging purpose, which is a simpler problem requiring just encoder and generator.
A first version is available here: https://github.com/OpenNMT/OpenNMT/pull/155
@Wabbit - do you want to try?
@jean.senellart sure I'll try and provide feedback. However, why do you think attention might not be required for tagging? The last hidden layer of the encoder might not carry enough information and additional access to the source side hidden states might be required. See a recent NAACL paper Neural Architectures for Named Entity Recognition which feeds in the hidden state from source at timestep t when decoding for step 't'
Hi @Wabbit - just saw your question. Indeed we could also think about having an attention module in a sequence tagger or use the full seq2seq approach for tagging - but the sequence tagger proposed here is a more simple (and conventional) approach...
hi @jean.senellart ,
Do you have any papers or document related to Sequence tagging that you implemented on ONMT?I want to know more about this feature.
Thank a lot.
Have you found a way to implement sequence tagging using OpenNMT? I am looking for any examples/documentation for the same.
@pankajkumar - sequence tagging is part of the toolkit.
let me know if you have any problem to run this.
Can the seqtagger model:
not today - but adding a wrapping like the translation server is trivial
yes - the same way than seq2seq models
It would be great. Otherwise, tagging sentences on demand, one by one, would force to load the model for each sentence, isn’t it ? A bit heavy…