PBLM Domain Adaptation

PBLM-Domain-Adaptation

Penulis: Yftah Ziser, Roi Reichart (Technion - Institut Teknologi Israel).

Ini adalah repositori kode yang digunakan untuk menghasilkan hasil yang muncul dalam pemodelan bahasa berbasis pivot untuk peningkatan adaptasi domain saraf.

Jika Anda menggunakan implementasi ini di artikel Anda, silakan kutip :)

 @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 }
}

INSTALASI

PBLM membutuhkan paket berikut:

Python> = 2.7.

Numpy

SCIPY

Theano/TensorFlow

keras

scikit-learn

Contoh

Anda dapat menemukan contoh yang dijelaskan di 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 )