Sarcasm_Detection/SMSD

• Title: Sarcasm Detection with Self-matching Networks and Low-rank Bilinear Pooling
• • Introduce
  • Model
  • Experimental Results
  • Installation and Environment Configuration
  • Instructions for Use
  • Reference

Title: Sarcasm Detection with Self-matching Networks and Low-rank Bilinear Pooling

Introduce

Traditional approaches rely heavily on discrete handcrafted features and will incur enormous human costs. It was not until recent that scholars began to employ neural networks to address these limitations and have achieved new state-of-the-art performance. In this work, we propose a novel self-matching network to capture sentence incongruity information by exploring word-to-word interactions. In particular, we calculate the joint information in each word-to-word pair in the input sentence to build a self-matching attention vector, based on which we attend the sentence and build its representation vector. Such a network allows sentence to match within itself word by word and cater to the words of conflict sentiments. In addition, we incorporate a bi-directional LSTM network into our proposed network to retain compositional information. We concatenate incongruity information and compositional information through a Low-rank Bilinear Pooling method to control for potential information redundancy without losing discriminative power.

Model

• Sentence Representation
• Self-matching Network
• Bi-direction LSTM
• Low-rank Bilinear Pooling

Experimental Results

Installation and Environment Configuration

• Please refer to requirements.txt
• Download glove.42B.300d.txt from glove
• pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz

Instructions for Use

python3 train.py

Reference

@inproceedings{
    author = {Xiong, Tao and Zhang, Peiran and Zhu, Hongbo and Yang, Yihui},
    title = {Sarcasm Detection with Self-Matching Networks and Low-Rank Bilinear Pooling},
    year = {2019},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3308558.3313735},
    doi = {10.1145/3308558.3313735},
    pages = {2115–2124},
    numpages = {10},
    keywords = {self-matching, Sarcasm detection, co-attention, Bi-LSTM},
    series = {WWW '19}
}