We study the task of span-level Emotion cause analysis (SECA), which is focused on extracting the specific emotion cause span(s) for a certain emotion expressed in the given context. Compared to popular clause-level emotion cause analysis (CECA), it is a finer-grained emotion cause analysis (ECA) task. The existing SECA method heavily dependents on the effectiveness of designed features, which is labor-intensive and not generalized well. In this paper, we formalize SECA as a sequence tagging task for which several variants of neural network-based sequence tagging models to extract specific emotion cause span(s) in the given context. These models combine different types of encoding and decoding approaches. Furthermore, to make our models more ``emotionally sensitive'', we utilize the multi-head attention mechanism to enhance the representation of context. Experimental evaluations conducted on two benchmark datasets show that our proposed models create new state-of-the-art results. Our work is the first using neural sequence tagging method for span-level ECA.

@inproceedings{li-cikm21b, abstract = {We study the task of span-level Emotion cause analysis (SECA), which is focused on extracting the specific emotion cause span(s) for a certain emotion expressed in the given context. Compared to popular clause-level emotion cause analysis (CECA), it is a finer-grained emotion cause analysis (ECA) task. The existing SECA method heavily dependents on the effectiveness of designed features, which is labor-intensive and not generalized well. In this paper, we formalize SECA as a sequence tagging task for which several variants of neural network-based sequence tagging models to extract specific emotion cause span(s) in the given context. These models combine different types of encoding and decoding approaches. Furthermore, to make our models more ``emotionally sensitive'', we utilize the multi-head attention mechanism to enhance the representation of context. Experimental evaluations conducted on two benchmark datasets show that our proposed models create new state-of-the-art results. Our work is the first using neural sequence tagging method for span-level ECA.}, address = {Online}, author = {Xiangju Li and Wei Gao and Shi Feng and Wang Daling and Shafiq Joty}, booktitle = {Proceedings of The 30th ACM International Conference on Information and Knowledge Management}, month = {November}, pages = {xx--xx}, publisher = {ACM}, series = {CIKM'21 (short paper)}, title = {Span-level Emotion Cause Analysis with Neural Sequence Tagging}, url = {}, year = {2021} }