An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Table 4: F1-I scores of different model variants. Average results over 5 runs are reported.
['Model variants', 'D1', 'D2', 'D3']
Ablation study. To investigate the impact of different components, we start with a vanilla model which consists of fθs, fθae, and fθas only without any informative message passing, and add other components one at a time. + Message passing-a denotes propagating the outputs from aspect-level tasks only at each message passing iteration. +DS and +DD denote adding DS and DD with parameter sharing only. +Message passing-d denotes involving the document-level information for message passing. We observe that +Message passing-a and +Message passing-d contribute to the performance gains the most, which demonstrates the effectiveness of the proposed message passing mechanism. We also observe that simply adding document-level tasks (+DS/DD) with parameter sharing only marginally improves the performance of IMN−d. This again indicates that domain-specific knowledge has already been captured by domain embeddings, while knowledge obtained from DD and DS via parameter sharing could be redundant in this case. However, +Message passing-d is still helpful with considerable performance gains, showing that aspect-level tasks can benefit from knowing predictions of the relevant document-level tasks.