In this paper, we revisit sarcasm detection from a novel perspective, so as to account for the long-range literal sentiment inconsistencies. More concretely, we explore a novel scenario of constructing an affective graph and a dependency graph for each sentence based on the affective information retrieved from external affective commonsense knowledge and the syntactical information of the sentence. Based on it, an Affective Dependency Graph Convolutional Network (ADGCN) framework is proposed to draw long-range incongruity patterns and inconsistent expressions over the context for sarcasm detection by means with interactively modeling the affective and dependency information.
As demonstrated in Figure 2, the architecture of the proposed ADGCN framework contains three main components:
glove.42B.300d.txt
from glovepython3 dependency_graph.py
python3 train.py
@inproceedings{
author = {Lou, Chenwei and Liang, Bin and Gui, Lin and He, Yulan and Dang, Yixue and Xu, Ruifeng},
title = {Affective Dependency Graph for Sarcasm Detection},
year = {2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3404835.3463061},
booktitle = {Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1844–1849},
series = {SIGIR '21}
}
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