QuASE: Question-Answer Driven Sentence Encoding
Table 5: The potential of further improving QuASE{}_{\small QAMR} by further pre-training it on more QA data. The “+” between datasets means union with shuffling. Both Large QA-SRL and QA-RE help achieve better results than QAMR alone. For simplicity, we use a simple BiLSTM model for SRL and a simple CNN model for RE. See more in Appendix B.
['Tasks', 'SRL', 'SRL', 'RE', 'RE']
We investigate whether adding the Large QA-SRL dataset FitzGerald et al. We use s-QuASE embeddings to replace BERT embeddings instead of concatenating the two embeddings. We find that adding related QA signals (Large QA-SRL for SRL and QA-RE for RE) into QAMR can help improve specific tasks. Noteworthy is the fact that QA-RE can help SRL (Large QA-SRL can also help RE), though the improvement is minor compared to Large QA-SRL (QA-RE). These results indicate that adding more QA signals related to the sentence can help get a better sentence representation in general.