SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis
Table 4: Ablation test on sentence-level / aspect-level sentiment analysis tasks, where F1, accuracy (Acc.) and Macro-F1 (MF1.) are reported (%). The early fusion (EF) / late supervision (LS) sub-task and the linguistic knowledge (Know.) are removed in different settings, respectively.
['Task Dataset', 'SSC MR', 'ATE Res14', 'ATSC Res14', 'ATSC Res14', 'ACD Res16', 'ACSC Res14', 'ACSC Res14']
Since the two sub-tasks are separate, the setting of -EF Additionally, the -Know. setting means that we removed the part of speech and word-level sentiment polarity in the input embeddings as well as in the supervision signals of the two sub-tasks, and reset the probability of masking sentiment words to 15%. The late supervision sub-task plays a more important role in most of the tasks including sentence-level / aspect-level sentiment analysis. Intuitively, compared with early fusion, the setting of late supervision is closer to sentiment classification, thereby contributing more to the performance on the classification tasks.