Papers with AI task
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering (D19-60)
Copied to clipboard
| Challenge: | Recent approaches on non-extractive commonsense QA show increased performance . attention-based injection seems to be preferable for knowledge integration . |
| Approach: | They propose to use attention-based injection to integrate knowledge into commonsense QA models. |
| Outcome: | The proposed methods show that attention-based injection is preferable for knowledge integration, and that the degree of domain overlap plays a crucial role in determining model success. |
One-shot Learning for Question-Answering in Gaokao History Challenge (C18-1)
Copied to clipboard
| Challenge: | Existing work on deep question-answering tasks from admission exams is challenging since it requires effective representation to capture complicated semantic relations between questions and answers. |
| Approach: | They propose a hybrid neural model for deep question-answering task from history examinations using a gated network and a machine labeler. |
| Outcome: | The proposed model obtains substantial performance gains over baseline models in terms of multiple evaluation metrics. |