Fetching the paper…
Reading the bibliography…
Sarcasm Detection has enjoyed great interest from the research community, however the task of predicting sarcasm in a text remains an elusive problem for machines.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang. 2010 · 2010
Earlier work this paper cites.
The perfect solution for detecting sarcasm in tweets #not. In WASSA@NAACL-HLT
Christine Liebrecht, Florian Kunneman, and Antal van den Bosch. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) , Vol. 2. 757–762
Aditya Joshi, Vinita Sharma, and Pushpak Bhattacharyya. 2015 · 2015
Cited alongside, same era.
Modelling context with user embeddings for sarcasm detection in social media
Silvio Amir, Byron C Wallace, Hao Lyu, and Paula Carvalho Mário J Silva. 2016 · 2016
Cited alongside, same era.
Automatic sarcasm detection: A survey
Aditya Joshi, Pushpak Bhattacharyya, and Mark J Carman. 2017 · 2017
Cited alongside, same era.
Comparative study of cnn and rnn for natural language processing
Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze. 2017 · 2017
Later among the works it cites.
Augmenting end-to-end dialog systems with commonsense knowledge
Tom Young, Erik Cambria, Iti Chaturvedi, Minlie Huang, Hao Zhou, and Subham Biswas. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…