PBLM Domain Adaptation

PBLM-Domain-Adaptation

Autoren: Yftah Ziser, Roi Reichart (Technion - Israel Institute of Technology).

Dies ist ein Code -Repository, mit dem die in Pivot -basierten Sprachmodellierung erscheinen Ergebnisse für eine verbesserte Anpassung der neuronalen Domänen erzeugt werden.

Wenn Sie diese Implementierung in Ihrem Artikel verwenden, zitieren Sie bitte :)

 @inproceedings { ziser2018pivot ,
  title = { Pivot Based Language Modeling for Improved Neural Domain Adaptation } ,
  author = { Ziser, Yftah and Reichart, Roi } ,
  booktitle = { Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) } ,
  volume = { 1 } ,
  pages = { 1241--1251 } ,
  year = { 2018 }
}

INSTALLATION

PBLM benötigt die folgenden Pakete:

Python> = 2,7.

Numpy

Scipy

Theano/TensorFlow

Keras

Scikit-Learn

Beispiel

Sie finden ein erklärtes Beispiel in Run.py:

 import tr
import sentiment
import pre
import os
import itertools

if __name__ == '__main__' :
    domain = []
    domain . append ( "books" )
    domain . append ( "kitchen" )
    domain . append ( "dvd" )
    domain . append ( "electronics" )

    # training the PBLM model in order to create structure aware representation for domain adaptation
    #input:
    # shared representation for both source domain and target domain
    # first param: the source domain
    # second param: the target domain
    # third param: number of pivots
    # fourth param: appearance threshold for pivots in source and target domain
    # fifth param: the embedding dimension
    # sixth param: maximum number of words to work with
    # seventh param: maximum review length to work with
    # eighth param: hidden units number for the PBLM model
    #output: the software will create corresponding directory with the model
    tr . train_PBLM ( domain [ 0 ], domain [ 1 ], 500 , 10 , 256 , 10000 , 500 , 256 )

    # training the sentiment cnn using PBLM's representation
    # shared representation for both source domain and target domain
    # this phase needs a corresponding trained PBLM model in order to work
    # first param: the source domain
    # second param: the target domain
    # third param: number of pivots
    # fourth param: maximum review length to work with
    # fifth param: the embedding dimension
    # sixth param: maximum number of words to work with
    # seventh param: hidden units number for the PBLM model
    # eighth param: the number of filters for the CNN
    # ninth param: the kernel size for the CNN
    # output: the results file will be created in the same directory
    # of the model under the results directory in the "cnn" dir
    sentiment . PBLM_CNN ( domain [ 0 ], domain [ 1 ], 500 , 500 , 256 , 10000 , 256 , 250 , 3 )

    # training the sentiment LSTM using PBLM's representation
    # shared representation for both source domain and target domain
    # this phase needs a corresponding trained PBLM model in order to work
    # first param: the source domain
    # second param: the target domain
    # third param: number of pivots
    # fourth param: maximum review length to work with
    # fifth param: the embedding dimension
    # sixth param: maximum number of words to work with
    # seventh param: hidden units number for the PBLM model
    # eighth param: hidden units number for the lstm model
    # output: the results file will be created in the same directory
    # of the model under the results directory in the "lstm" dir
    sentiment . PBLM_LSTM ( domain [ 0 ], domain [ 1 ], 500 , 500 , 256 , 10000 , 256 , 256 )