Latent Variable Sentiment Grammar    Work was done when the first author was visiting Westlake University. The third author is the corresponding author.
Table 3: Experimental results with ELMo. BCN(P) is the BCN implemented by Peters et al. (2018). BCN(O) is the BCN implemented by ourselves.
['Model', 'SST-5 Root', 'SST-5 Phrase', 'SST-2 Root', 'SST-2 Phrase']
There has also been work using large-scale external datasets to improve performances of sentiment classification. Peters et al. combined bi-attentive classification network (BCN, McCann et al. with a pretrained language model with character convolutions on a large-scale corpus (ELMo) and reported an accuracy of 54.7 on sentence-level SST-5. For fair comparison, we also augment our model with ELMo. BCN+WG improves accuracies on all task slightly by modeling sentiment composition explicitly. The obvious promotion of BCN+LVG4 and BCN+LVeG shows that explicitly modeling sentiment composition with fine-grained sentiment subtypes is useful. Particularly, BCN+LVeG improves the sentence level classification accurracies by 1.4 points (fine-grained) and 0.7 points (binary) compared to BCN (our implementation), respectively. To our knowledge, we achieve the best results on the SST dataset.