Papers with TrecQA
Self-Training for Jointly Learning to Ask and Answer Questions (N18-1)
Copied to clipboard
| Challenge: | Existing methods for question answering and question generation are hard to obtain in many domains. |
| Approach: | They propose a method for jointly learning to ask and answer questions . they leverage unlabeled text along with labeled question answer pairs for learning . |
| Outcome: | The proposed method improves on four benchmark datasets on question answering and question generation tasks. |
Recursive Tree-Structured Self-Attention for Answer Sentence Selection (2021.acl-long)
Copied to clipboard
| Challenge: | Recent top-performing models in Answer Sentence Selection use self-attention and transfer learning, but not syntactic structure. |
| Approach: | They propose a recursive, tree-structured self-attention model that can represent all levels of syntactic parse trees with only one additional layer. |
| Outcome: | The proposed model can represent all levels of syntactic parse trees with only one additional layer without transfer learning. |
A Gated Self-attention Memory Network for Answer Selection (D19-1)
Copied to clipboard
| Challenge: | Existing deep learning approaches for answer selection use word-level comparison followed by aggregation. |
| Approach: | They propose a new gated self-attention memory network for answer selection task . they combine a transfer learning technique from a large-scale online corpus to create a gated network . |
| Outcome: | The proposed model outperforms existing methods on two standard answer selection datasets: TrecQA and WikiQA. |