@InProceedings{ghosh-richardfabbri-muresan:2017:W17-55,
  author    = {Ghosh, Debanjan  and  Richard Fabbri, Alexander  and  Muresan, Smaranda},
  title     = {The Role of Conversation Context for Sarcasm Detection in Online Interactions},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {186--196},
  abstract  = {Computational models for sarcasm detection have often relied on the content of
	utterances in isolation. However, speaker's sarcastic intent is not always
	obvious without additional context. Focusing on social media discussions, we
	investigate two issues: (1) does modeling of conversation context help in
	sarcasm detection and (2) can we understand what part of conversation context
	triggered the sarcastic reply. To address the first issue, we investigate
	several types of Long Short-Term Memory (LSTM) networks that can model both the
	conversation context and the sarcastic response. We show that the conditional
	LSTM network (Rockt\"{a}schel et al. 2015) and LSTM networks with sentence level
	attention on context and response outperform the LSTM model that reads only the
	response. To address the second issue, we present a qualitative analysis of
	attention weights produced by the LSTM models with attention and discuss the
	results compared with human performance on the task.},
  url       = {http://aclweb.org/anthology/W17-5523}
}