Multitask Learning for Aspect-Based Sentiment Classification

Table 4

Performance comparison with previous models, with average accuracy as the evaluation metric; the best result for each dataset is boldfaced.

	Method	14Lap	14Res	15Res
Conventional network	TD-LSTM (2016)	68.13	75.63	76.39
	ATAE-LSTM (2016)	68.70	77.20	78.48
	MemNet (2016)	70.33	78.16	77.31
	TNET (2016)	74.65	80.05	78.47
	IAN (2017)	72.10	78.60	78.58
	RAM (2017)	74.49	80.23	79.98
	TG-SAN (2020)	75.27	81.66	—
Multitask learning	PRET + MULT (2018)	71.15	79.11	81.30
BERT	IMN (2019)	75.36	83.89	85.64
	BERT-FC (2018)	76.54	81.28	81.52
	BERT-pair-QA-M (2019)	77.93	85.12	81.89
	AEN-BERT (2019)	78.35	81.46	—
	BERT-PT (2019)	78.07	84.95	—
	TD-BERT (2019)	78.87	85.10	—
	SK-GCN-BERT (2020)	79.00	83.48	83.20
	SPRN-BERT (2021)	79.31	85.03	85.30
Our model	SMLN	80.09	85.67	86.31