Deep Semantic Role Labeling (SRL)
SRL deep learning model is based on DB-LSTM which is described in this paper : End-to-end learning of semantic role labeling using recurrent neural networks
The system consists of code related to two models for following tasks :
Predicate identification
Model trained using RNN network with CRF as final output layer
SRL model
SRL Trained on db-lstm network with CRF as final output layer.
Evaluation
We use evaluation/conlleval.pl for model evaluation for both the models . This script is taken from Conll2000 shared task. (http://www.cnts.ua.ac.be/conll2000/ ) Rest of the code files for data manipulation and feeding the data for neural networks.
Code Layout :
Data folder :
embeddings :
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The word embeddings have been trained using fasttext by facebook . The word embedding are represented by two files : Vocab.txt : the vocabulary words in w2v
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wordVectors.txt : the vector for words mentioned in vocab.txt. Please note that it is row-row mapping from word to vector.
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The w2v model is trained with skipgram , 32 dimensions on QQ+SRL data
Predicate_identifier
Data for predicate identifier model in conll05 format. Please note that this format is generated by SemanticRoleLabeler Srl
Models folder :
- Final - The final models for predicate identification and srl model
- Parameters- This folder is for saving model parameters while training
- PI - Folder to save predicate identifier model while training
- Srl - Folder to save srl model parameters while training
Paddle Docker
THe deep learning code is built using Paddle framework by Baidu (https://github.com/PaddlePaddle/Paddle)
Paddle docker is required to run the code in Docker environment.
Working in the docker environment: Go to semantic-role-labeling-neural/ - Run following command + docker run -it -v $(pwd):/paddle paddlepaddle/paddle:0.10.0 /bin/bash
if the docker image is not already present on the system, it will be downloaded. Also please check for newer version of the docker image
Model related code :
Predicate Identifier Model
Training the model :
pi_train.py
Running inference code from trained model :
Pi_infer.py
SRL model
Training the model
Srl_train.py
Running inference code from trained model :
Srl_inference.py