  Computational models for sarcasm detection have often relied on the content
of utterances in isolation. However, the speaker's sarcastic intent is not
always apparent without additional context. Focusing on social media
discussions, we investigate three issues: (1) does modeling conversation
context help in sarcasm detection; (2) can we identify what part of
conversation context triggered the sarcastic reply; and (3) given a sarcastic
post that contains multiple sentences, can we identify the specific sentence
that is sarcastic. 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 current turn. We show that LSTM networks with sentence-level
attention on context and current turn, as well as the conditional LSTM network
(Rocktaschel et al. 2016), outperform the LSTM model that reads only the
current turn. As conversation context, we consider the prior turn, the
succeeding turn or both. Our computational models are tested on two types of
social media platforms: Twitter and discussion forums. We discuss several
differences between these datasets ranging from their size to the nature of the
gold-label annotations. To address the last two issues, 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 two
tasks.
