We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| is_prediction | Ran | this paper's copy was not recorded; identical code first harvested from Linear95/SPAG pointer only · get_code("3f80297da8f27692") |
| load_keyword_list | Ran | this paper's copy was not recorded; identical code first harvested from Linear95/SPAG pointer only · get_code("2dbc36d1deee2171") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker must hide its intention and subtly induce the defender, while a competitive defender must be cautious with its utterances and infer the intention of the attacker. Such language abilities can facilitate many important downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments and empirical studies on several baseline attack and defense strategies show promising and interesting results. Based on the analysis on the game and experiments, we discuss multiple promising directions for future research.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1911.01622")
get_code_for_paper("1911.01622")
have("1911.01622")
Connect an agent — have() is free.