Attentional Multi-Reading Sarcasm Detection
This addresses the problem of sarcasm detection in natural language processing for applications like social media analysis, but it is incremental as it builds on limited prior attempts to incorporate context.
The paper tackled sarcasm detection by proposing an interpretable end-to-end model that combines utterance and conversational context, demonstrating effectiveness through empirical evaluations and providing explanations for model decisions.
Recognizing sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and real world facts. Most of the current sarcasm detection systems consider only the utterance in isolation. There are some limited attempts toward taking into account the conversational context. In this paper, we propose an interpretable end-to-end model that combines information from both the utterance and the conversational context to detect sarcasm, and demonstrate its effectiveness through empirical evaluations. We also study the behavior of the proposed model to provide explanations for the model's decisions. Importantly, our model is capable of determining the impact of utterance and conversational context on the model's decisions. Finally, we provide an ablation study to illustrate the impact of different components of the proposed model.