parser doesn't produce amr-unknown about transition-amr-parser HOT 5 OPEN

parsing the same sentence with amrlib parser, for example, gives me this result with amr-unknown:

# ::snt Which architect of Marine Corps Air Station Kaneohe Bay was also tenant of New Sanno hotel?
(t / tenant-01
      :ARG0 (a / amr-unknown
            :ARG0-of (a2 / architect-01
                  :ARG1 (f / facility
                        :name (n / name
                              :op1 "Marine"
                              :op2 "Corps"
                              :op3 "Air"
                              :op4 "Station"
                              :op5 "Kaneohe"
                              :op6 "Bay"))))
      :ARG1 (h / hotel
            :name (n2 / name
                  :op1 "New"
                  :op2 "Sanno"))
      :mod (a3 / also))

from transition-amr-parser.

It should produce amr-unknown, we use this often for question parsing.

What did you trained it with? I just checked on a v0.4.2 deploy and it parses correctly. Also, do you tokenize?

from transition-amr-parser.

hi @ramon-astudillo, well, I was trying to follow your setup instructions from here for setup and training (the default action-pointer network config bash run/run_experiment.sh configs/amr2.0-action-pointer.sh ). This is the code for inference:

from transition_amr_parser.parse import AMRParser
amr_parser_checkpoint = "/DATA/AMR2.0/models/exp_cofill_o8.3_act-states_RoBERTa-large-top24/_act-pos-grh_vmask1_shiftpos1_ptr-lay6-h1_grh-lay123-h2-allprev_1in1out_cam-layall-h2-abuf/ep120-seed42/checkpoint_best.pt"
parser = AMRParser.from_checkpoint(amr_parser_checkpoint)
words = [word.strip(string.punctuation) for word in text.split()]
annotations = parser.parse_sentences([words])

from transition-amr-parser.

would mind sharing your trained checkpoint to see if it makes any difference?

from transition-amr-parser.

would mind sharing your trained checkpoint to see if it makes any difference?

I am certain it should. We are looking into sharing pre-trained models but I can not say anything at this point.

Also FYI we will update to v0.5.1 soon (post EMNLP preprint submission deadline). This new model (Structured-BART) is new SoTA for AMR2.0 and will be published at EMNLP2021, a non updated prerprint is here https://openreview.net/forum?id=qjDQCHLXCNj

From experience in parsing questions, I can say silver-data fine-tuning works well. You can parse some text corpus with questions, filter it with a couple of rules*, and the use it as additional training data. The training scheme silver+gold pre-training with gold fine-tuning seems to work best, see e.g. https://aclanthology.org/2020.findings-emnlp.288/

(*) For example ignore all parses having :rel (which indicates a detached subgraph) or with missing amr-unknown (if you are certain it should have one).

from transition-amr-parser.