  Sarcasm employs ambivalence, where one says something positive but actually
means negative, and vice versa. The essence of sarcasm, which is also a
sufficient and necessary condition, is the conflict between literal and implied
sentiments expressed in one sentence. However, it is difficult to recognize
such sentiment conflict because the sentiments are mixed or even implicit. As a
result, the recognition of sophisticated and obscure sentiment brings in a
great challenge to sarcasm detection. In this paper, we propose a Dual-Channel
Framework by modeling both literal and implied sentiments separately. Based on
this dual-channel framework, we design the Dual-Channel Network~(DC-Net) to
recognize sentiment conflict. Experiments on political debates (i.e. IAC-V1 and
IAC-V2) and Twitter datasets show that our proposed DC-Net achieves
state-of-the-art performance on sarcasm recognition. Our code is released to
support research.
