Papers with real-world debates
Conclusion-based Counter-Argument Generation (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. |
| Approach: | They propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. |
| Outcome: | The proposed approach generates more relevant and stance-adhering counters than strong baselines. |