Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification
Table 2. [Color online] Accuracy and F1 scores with ELMO as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure.
['[EMPTY]', '[EMPTY]', 'Target domains ACAD', 'Target domains ACAD', 'Target domains WEAT', 'Target domains WEAT', 'Target domains ADLD', 'Target domains ADLD', 'Target domains IMDB', 'Target domains IMDB', 'Target domains YELP', 'Target domains YELP', 'Target domains SCCD', 'Target domains SCCD']
We, first of all, discuss the performance score of EMLO-based model. Here, each target domain represents 15% test data. Only training on WLD datasets produced worst results (see the row with source as AWLD). Our proposed training procedure (for example, ACAD-AWLD) produced marginal performance. We witness similar poor and marginal performances for other WLDs and for our other proposed two-stage experiment settings, respectively. We claim that the poorer EMLO’s performance gains in cross-domain classification are primarily due to its current limitations in generalizability as compare to other state-of-the-art embeddings generated from transformer-based language models. Next, we experiment with other competitive models.