  Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their
sentiment polarities and opinions explaining the sentiment from a sentence.
ASTE could be naturally divided into 3 atom subtasks, namely target detection,
opinion detection and sentiment classification. We argue that the proper
subtask combination, compositional feature extraction for target-opinion pairs,
and interaction between subtasks would be the key to success. Prior work,
however, may fail on `one-to-many' or `many-to-one' situations or derive
non-existent sentiment triplets due to defective subtask formulation,
sub-optimal feature representation or the lack of subtask interaction. In this
paper, we divide ASTE into target-opinion joint detection and sentiment
classification subtasks, which is in line with human cognition, and
correspondingly utilize sequence encoder and table encoder to handle them.
Table encoder extracts sentiment at token-pair level, so that the compositional
feature between targets and opinions can be easily captured. To establish
explicit interaction between subtasks, we utilize the table representation to
guide the sequence encoding, and inject the sequence features back into the
table encoder. Experiments show that our model outperforms state-of-the-art
methods on six popular ASTE datasets.
