Name
Abstract | ||
|---|---|---|
Current commonsense reasoning research mainly focuses on developing models that use commonsense knowledge to answer multiple-choice questions. However, systems designed to answer multiple-choice questions may not be useful in applications that do not provide a small list of possible candidate answers to choose from. As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) -- the task of answering a commonsense question without any pre-defined choices, using as a resource only a corpus of commonsense facts written in natural language. The task is challenging due to a much larger decision space, and because many commonsense questions require multi-hop reasoning. We propose an efficient differentiable model for multi-hop reasoning over knowledge facts, named DrFact. We evaluate our approach on a collection of re-formatted, open-ended versions of popular tests targeting commonsense reasoning, and show that our approach outperforms strong baseline methods by a large margin. |
Year | Venue | DocType |
|---|---|---|
2021 | NAACL-HLT | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Chen Lin | 1 | 28 | 11.20 |
| Sun, Haitian | 2 | 13 | 4.28 |
| Bhuwan Dhingra | 3 | 159 | 13.39 |
| Manzil Zaheer | 4 | 160 | 23.65 |
| Xiang Ren | 5 | 885 | 60.08 |
| William W. Cohen | 6 | 10178 | 1243.74 |