Label-Reasoning-Network

This repository is the code for "Fine-grained Entity Typing via Label Reasoning" EMNLP2021

We evaluate the performance on two datasets: OntoNotes and Ultra-Fine from Choi. Considering the size of the data, we only put the Ultra-Fine in the folder data/

Data	Train	Dev	Test
Ultra-Fine	1998	1998	1998
OntoNotes	251039	2202	8963
OntoNotes augmentation	793487	2202	8963

Dependency

Configure the environment in python=3.7.7：

pip install -r requirement.txt

then download the Glove Vector and put it in the data/, here we use glove.6b.300d.txt

Test Model

If you want to run the model, download the mode and put it into model/:

bash view_typing.bash

Train Model

1. You can train a LRN w/o IR for example:

python -m entity_typing.train seq_typing \
  -model-path model/ultra_fine/seq_typing_entity_marker_macro_att1_att2 \
  -label-emb-size 100 \
  -bert-type entity_marker \
  -device 2 \
  -batch 32 \
  -patience 10 \
  -fine-tune-transformer \
  -transformer bert-base-uncased \
  -lr 1e-3 \
  -overwrite-model-path \
  -lr-diff \
  -edit 0.05 \
  -teaching-forcing-rate 0 \
  -seq-type sequence \
  -shuffle-num 0 \
  -loss-type match \
  -decoder-type lstm2 \
  -evaluation macro \
  $*

2. You can train a LRN for example:

python -m entity_typing.train mem_typing \
  -model-path model/ultra_fine/kv_typing_entity_marker_macro_att1_att2 \
  -label-emb-size 100 \
  -data-folder-path data/open_type_original_lemma_synthetic \
  -value-file probe_experiment/data/all_types.txt \
  -memory-emb data/glove.6B.300d.txt \
  -memory-emb-size 300 \
  -bert-type entity_marker_kv \
  -device 3 \
  -batch 32 \
  -patience 10 \
  -fine-tune-transformer \
  -transformer bert-base-uncased \
  -lr 1e-3 \
  -overwrite-model-path \
  -lr-diff \
  -edit 0.05 \
  -teaching-forcing-rate 0 \
  -seq-type sequence \
  -shuffle-num 0 \
  -loss-type match \
  -decoder-type lstm2 \
  -evaluation macro \
  -loss-lambda 0 \
  -loss-lambda2 1 \
  -similar-thred 0.2 \
  -sparce-rate 0.9 \
  $*

others

If you use other Glove Vector, edit the dimension of -memory-emb-size

If you want to train another model, you can refer to the ./train_*.bash file.

If you want use other attributes, you can add it to data and edit the process_line function in entity_typing/data_utils.py