  Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based
sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the
sentence, TOWE extracts the corresponding opinion words for the aspect term.
TOWE has two types of instance. In the first type, aspect terms are associated
with at least one opinion word, while in the second type, aspect terms do not
have corresponding opinion words. However, previous researches trained and
evaluated their models with only the first type of instance, resulting in a
sample selection bias problem. Specifically, TOWE models were trained with only
the first type of instance, while these models would be utilized to make
inference on the entire space with both the first type of instance and the
second type of instance. Thus, the generalization performance will be hurt.
Moreover, the performance of these models on the first type of instance cannot
reflect their performance on entire space. To validate the sample selection
bias problem, four popular TOWE datasets containing only aspect terms
associated with at least one opinion word are extended and additionally include
aspect terms without corresponding opinion words. Experimental results on these
datasets show that training TOWE models on entire space will significantly
improve model performance and evaluating TOWE models only on the first type of
instance will overestimate model performance.
