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 a multi-dimensional intra-attention recurrent network that models intricate similarities between each word pair in the sentence.
glove.42B.300d.txt
from glovepython3 train.py
@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",
}
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