Explicit Contextual Semantics for Text Comprehension
Table 4: Ablation study. Since we use ELMo as the basic word embeddings, we replace ELMO with 300D GloVe embeddings for the case -ELMo.
['Model', 'Dev', 'Test']
To evaluate the contributions of key factors in our method, a series of ablation studies are performed on the SNLI dev and test set. We observe both SRL and ELMo embeddings contribute to the overall performance. Note that ELMo is obtained by deep bidirectional language with 4,096 hidden units on a large-scale corpus, which requires long training time with 93.6 million parameters. The output dimension of ELMo is 512. Compared with the massive computation and high dimension, SRL embedding is much more convenient for training and much easier for model integration, giving the same level of performance gains.