Evaluation Benchmarks and Learning Criteriafor Discourse-Aware Sentence Representations
Table 2: Results for SentEval and DiscoEval. The highest number in each column is boldfaced. The highest number for our models in each column is underlined. “All” uses all four losses. “avg.” is the averaged accuracy for all tasks in DiscoEval.
['[EMPTY]', 'SentEval USS', 'SentEval SSS', 'SentEval SC', 'SentEval Probing', 'DiscoEval SP', 'DiscoEval BSO', 'DiscoEval DC', 'DiscoEval SSP', 'DiscoEval PDTB-E', 'DiscoEval PDTB-I', 'DiscoEval RST-DT', 'DiscoEval avg.']
We train our models on Wikipedia as it is a knowledge rich textual resource and has consistent structures over all documents. Details on hyperparameters are in the supplementary material. When evaluating on DiscoEval, we encode sentences with pretrained sentence encoders. Following SentEval, we freeze the sentence encoders and only learn the parameters of the downstream classifier. Different models and training signals have complex effects when performing various downstream tasks. We summarize our findings below: