Subword_Semantic_Role_Labeling

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Semantic role labeling with subwords (character, character-ngram and morphology)

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## Semantic Role Labeling with subword composition

The code for our ACL18 paper ["Character-Level Models versus Morphology in Semantic Role Labeling"](https://arxiv.org/abs/1805.11937).

We provide sample training/testing scripts for different subword units under example_scripts folder.

### Scripts Overview 
Train SRL models on CoNLL-09 SRL training sets and test/evaluate trained models on CoNLL-09 evaluation sets for all languages.

1. **simple_UNIT.sh**: Trains/tests *base SRL models* for the given subword UNIT. Please check train.py for parameter descriptions.

2. **ensemble_UNIT1_UNIT2_UNITn**: Voting ensemble for the provided pretrained base SRL models (UNIT1, UNIT2, ..., UNITn).

3. **sg_UNIT1_UNIT2_UNITn**: Trains/tests a stack generalizer model from the predictions of pretrained base SRL models (UNIT1, UNIT2, ..., UNITn).

### Testing Environment

*Language* : Python 2.7  
*CUDA*     : Cuda compilation tools, release 8.0, V8.0.44  
*Libraries*: PyTorch 0.2.0 post 3, numpy 1.13.0