A PyTorch implementation of the models for the paper "Matching the Blanks: Distributional Similarity for Relation Learning" published in ACL 2019.
Note: This is not an official repo for the paper.
Additional models for relation extraction, implemented here based on the paper's methodology:
- ALBERT (https://arxiv.org/abs/1909.11942)
Requirements: Python (3.6+), PyTorch (1.2.0), Spacy (2.1.8)
Pre-trained BERT(S) model courtesy of HuggingFace.co (https://huggingface.co)
Run main_pretraining.py with arguments below. Pre-training data can be any .txt continuous text file.
We use Spacy NLP to grab pairwise entities (within a window size of 40 tokens length) from the text to form relation statements for pre-training. Entities recognition are based on NER and dependency tree parsing of objects/subjects.
The pre-training data (cnn.txt) that I've used can be downloaded here.
Note: Pre-training can take a long time, depending on available GPU. It is possible to directly fine-tune on the relation-extraction task and still get reasonable results, following the section below.
main_pretraining.py [-h]
[--pretrain_data TRAIN_PATH]
[--batch_size BATCH_SIZE]
[--freeze FREEZE]
[--gradient_acc_steps GRADIENT_ACC_STEPS]
[--max_norm MAX_NORM]
[--fp16 FP_16]
[--num_epochs NUM_EPOCHS]
[--lr LR]
[--model_no MODEL_NO (0: BERT ; 1: ALBERT)]Run main_task.py with arguments below. Requires SemEval2010 Task 8 dataset, available here. Download & unzip to ./data/ folder.
main_task.py [-h]
[--train_data TRAIN_DATA]
[--test_data TEST_DATA]
[--use_pretrained_blanks USE_PRETRAINED_BLANKS]
[--num_classes NUM_CLASSES]
[--batch_size BATCH_SIZE]
[--gradient_acc_steps GRADIENT_ACC_STEPS]
[--max_norm MAX_NORM]
[--fp16 FP_16]
[--num_epochs NUM_EPOCHS]
[--lr LR]
[--model_no MODEL_NO (0: BERT ; 1: ALBERT)]
[--train TRAIN]
[--infer INFER]To infer a sentence, you can annotate entity1 & entity2 of interest within the sentence with their respective entities tags [E1], [E2]. Example:
Type input sentence ('quit' or 'exit' to terminate):
The surprise [E1]visit[/E1] caused a [E2]frenzy[/E2] on the already chaotic trading floor.
Sentence: The surprise [E1]visit[/E1] caused a [E2]frenzy[/E2] on the already chaotic trading floor.
Predicted: Cause-Effect(e1,e2) from src.tasks.infer import infer_from_trained
inferer = infer_from_trained(args, detect_entities=False)
test = "The surprise [E1]visit[/E1] caused a [E2]frenzy[/E2] on the already chaotic trading floor."
inferer.infer_sentence(test, detect_entities=False)Sentence: The surprise [E1]visit[/E1] caused a [E2]frenzy[/E2] on the already chaotic trading floor.
Predicted: Cause-Effect(e1,e2) The script can also automatically detect potential entities in an input sentence, in which case all possible relation combinations are inferred:
inferer = infer_from_trained(args, detect_entities=True)
test2 = "After eating the chicken, he developed a sore throat the next morning."
inferer.infer_sentence(test2, detect_entities=True)Sentence: [E2]After eating the chicken[/E2] , [E1]he[/E1] developed a sore throat the next morning .
Predicted: Other
Sentence: After eating the chicken , [E1]he[/E1] developed [E2]a sore throat[/E2] the next morning .
Predicted: Other
Sentence: [E1]After eating the chicken[/E1] , [E2]he[/E2] developed a sore throat the next morning .
Predicted: Other
Sentence: [E1]After eating the chicken[/E1] , he developed [E2]a sore throat[/E2] the next morning .
Predicted: Other
Sentence: After eating the chicken , [E2]he[/E2] developed [E1]a sore throat[/E1] the next morning .
Predicted: Other
Sentence: [E2]After eating the chicken[/E2] , he developed [E1]a sore throat[/E1] the next morning .
Predicted: Cause-Effect(e2,e1) - Base architecture: BERT base uncased (12-layer, 768-hidden, 12-heads, 110M parameters)
Without MTB pre-training: F1 results when trained on 100 % training data:
- Base architecture: ALBERT base uncased (12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters)
Without MTB pre-training: F1 results when trained on 100 % training data:
- inference & results on benchmarks (SemEval2010 Task 8) with MTB pre-training
- felrel task
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