Name
Abstract | ||
|---|---|---|
ABSTRACTIn this paper, we introduce LiteratureQA, a large question answering (QA) corpus consisting of publicly available academic papers. Different from other QA corpus, LiteratureQA has its unique challenges such as how to leverage the structured knowledge of citation networks. We further examine some popular QA method and present a benchmark approach of answering academic questions by combining both semantic text and graph knowledge to improve the prevalent pre-training model. We hope this resource could help research and development of tasks for machine reading over academic text. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3459637.3482007 | Conference on Information and Knowledge Management |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiwen Wang | 1 | 0 | 1.01 |
| Le Zhou | 2 | 0 | 0.34 |
| Weinan Zhang | 3 | 1228 | 97.24 |
| Xinbing Wang | 4 | 2642 | 214.43 |