A Span Selection Model for Semantic Role Labeling

Citation

• A Span Selection Model for Semantic Role Labeling
• Hiroki Ouchi (RIKEN AIP/Tohoku Univ.), Hiroyuki Shindo (NAIST) and Yuji Matsumoto (NAIST)
• In EMNLP 2018
• Conference paper: http://aclweb.org/anthology/D18-1191
• arXiv version: https://arxiv.org/abs/1810.02245

@InProceedings{D18-1191,
  author = 	"Ouchi, Hiroki
		and Shindo, Hiroyuki
		and Matsumoto, Yuji",
  title = 	"A Span Selection Model for Semantic Role Labeling",
  booktitle = 	"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"1630--1642",
  location = 	"Brussels, Belgium",
  url = 	"http://aclweb.org/anthology/D18-1191"
}

Prerequisites

• python3
• Theano
• h5py

Installation

conda create -n theano-py3 python=3.6
source activate theano-py3
conda install -c conda-forge theano
conda install -c anaconda h5py

Data

CoNLL-2005

• Treebank-2

CoNLL-2012

• OntoNotes Release 5.0
• We create the dataset by following the process described at http://cemantix.org/data/ontonotes.html

Word Representations

• SENNA
  • Download the software and make the word-embedding pair file as follows.
  • paste hash/words.lst embeddings/embeddings.txt > senna.emb.txt

• ELMo

Data Format

CoNLL-2005 Training & Development Sets

0:WORD 1:POS 2:PARSE 3:NE 4:FRAME 5:LEMMA 6-:ARGS
Ms.                NNP    (S1(S(NP*         *    -   -       (A0*
Haag               NNP            *)    (LOC*)   -   -          *)
plays              VBZ         (VP*         *    02  play     (V*)
Elianti            NNP         (NP*))       *    -   -       (A1*)
.                   .             *))       *    -   -          *

CoNLL-2005 Test Set (Not including FRAME ID)

0:WORD 1:POS 2:PARSE 3:NE 4:LEMMA 5-:ARGS
The                DT     (S1(S(NP*         *                 (A1*
finger-pointing    JJ             *)        *    -               *)
has                AUX         (VP*         *    -               *
already            RB        (ADVP*)        *    -        (AM-TMP*)
begun              VBN         (VP*))       *    begin         (V*)
.

CoNLL-2012 Training/Development/Test Sets

0:DOCUMENT 1:PART 2:INDEX 3:WORD 4:POS 5:PARSE 6:LEMMA 7:FRAME 8:SENSE 9:SPEAKER 10:NE 11-N:ARGS N:COREF
bc/cctv/00/cctv_0001   0   0           This    DT  (TOP(S(NP*         -    -   -   Speaker#1        *   (ARG2*   (61
bc/cctv/00/cctv_0001   0   1            map    NN           *)        -    -   -   Speaker#1        *        *)   61)
bc/cctv/00/cctv_0001   0   2      reflected   VBD        (VP*    reflect  01   1   Speaker#1        *      (V*)    -
bc/cctv/00/cctv_0001   0   3            the    DT        (NP*         -    -   -   Speaker#1        *   (ARG1*     -
bc/cctv/00/cctv_0001   0   4       European    JJ           *         -    -   -   Speaker#1    (NORP)       *     -
bc/cctv/00/cctv_0001   0   5    battlefield    NN           *         -    -   -   Speaker#1        *        *     -
bc/cctv/00/cctv_0001   0   6      situation    NN           *))       -    -   -   Speaker#1        *        *)    -
bc/cctv/00/cctv_0001   0   7              .     .           *))       -    -   -   Speaker#1        *        *     -

Usage

Training: span selection model

SENNA: python src/main.py --method span --mode train --train_data path/to/conll2005.train.txt --dev_data path/to/conll2005.dev.txt --data_type conll05 --drop_rate 0.1 --reg 0.0001 --hidden_dim 300 --n_layers 4 --halve_lr --word_emb path/to/senna --save --output_dir output

ELMo: python src/main.py --method span --mode train --train_data path/to/conll2005.train.txt --dev_data path/to/conll2005.dev.txt --data_type conll05 --drop_rate 0.1 --reg 0.0001 --hidden_dim 300 --n_layers 4 --halve_lr --train_elmo_emb path/to/elmo.conll2005.train.hdf5 --dev_elmo_emb path/to/elmo.conll2005.dev.hdf5 --save --output_dir output

Training: CRF model

SENNA: python src/main.py --method crf --mode train --train_data path/to/conll2005.train.txt --dev_data path/to/conll2005.dev.txt --data_type conll05 --drop_rate 0.1 --reg 0.0001 --hidden_dim 300 --n_layers 4 --halve_lr --word_emb path/to/senna --save --output_dir output

ELMo: python src/main.py --method crf --mode train --train_data path/to/conll2005.train.txt --dev_data path/to/conll2005.dev.txt --data_type conll05 --drop_rate 0.1 --reg 0.0001 --hidden_dim 300 --n_layers 4 --halve_lr --train_elmo_emb path/to/elmo.conll2005.train.hdf5 --dev_elmo_emb path/to/elmo.conll2005.dev.hdf5 --save --output_dir output

Predicting: span selection model

SENNA: python src/main.py --method span --mode test --test_data path/to/conll2005.test.txt --data_type conll05 --drop_rate 0.1 --hidden_dim 300 --n_layers 4 --output_dir output --output_fn conll2005.test --word_emb path/to/senna --load_label output/label_ids.txt --load_param output/param.epoch-0.pkl.gz

ELMo: python src/main.py --method span --mode test --test_data path/to/conll2005.test.txt --data_type conll05 --drop_rate 0.1 --hidden_dim 300 --n_layers 4 --output_dir output --output_fn conll2005.test --test_elmo_emb path/to/elmo.conll2005.test.hdf5 --load_label output/label_ids.txt --load_param output/param.epoch-0.pkl.gz

Predicting: CRF model

SENNA: python src/main.py --method crf --mode test --test_data path/to/conll2005.test.txt --data_type conll05 --drop_rate 0.1 --hidden_dim 300 --n_layers 4 --output_dir output --output_fn conll2005.test --word_emb path/to/senna --load_label output/label_ids.txt --load_param output/param.epoch-0.pkl.gz

ELMo: python src/main.py --method crf --mode test --test_data path/to/conll2005.test.txt --data_type conll05 --drop_rate 0.1 --hidden_dim 300 --n_layers 4 --output_dir output --output_fn conll2005.test --test_elmo_emb path/to/elmo.conll2005.test.hdf5 --load_label output/label_ids.txt --load_param output/param.epoch-0.pkl.gz

LICENSE

MIT License