Sarcasm_Detection/MIARN

• Title: Reasoning with Sarcasm by Reading In-between
• • Introduce
  • Model
  • Experimental Results
  • Installation and Environment Configuration
  • Instructions for Use
  • Reference

Title: Reasoning with Sarcasm by Reading In-between

Introduce

The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, this work revisits the notion of modeling contrast in order to reason with sarcasm.

• This paper proposes an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity.
• This work conducts extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus.

Model

This paper proposes a multi-dimensional intra-attention recurrent network that models intricate similarities between each word pair in the sentence.

• deep learning model aims to capture contrast and incongruity within end-to-end neural networks.
• self-targeted co-attention, which allows model to not only capture word-word relationships but also long-range dependencies.
• model produces interpretable attention maps which aid in the explainability of model outputs.

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{
    title = "Reasoning with Sarcasm by Reading In-Between",
    author = "Tay, Yi  and Luu, Anh Tuan  and Hui, Siu Cheung  and Su, Jian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1093",
    doi = "10.18653/v1/P18-1093",
    pages = "1010--1020",
}