Diplomat: A Dialogue Dataset for Situated PragMATic Reasoning

Pragmatic reasoning aims at resolving implicit meanings that commonly occur in real-life and is crucial for building communicative social agents. We introduce a new benchmark, Diplomat, aiming at a unified paradigm for pragmatic reasoning and situated conversational understanding. Compared with previous works that treat different figurative expressions (e.g., metaphor, sarcasm) as individual tasks, Diplomat provides a unified understanding towards general pragmatic understanding. Our dataset is created using Amazon Mechanical Turk ( AMT ), resulting in 4, 177 multi-turn dialogues. In company with the dataset, we propose two tasks: Pragmatic Identification and Reasoning and Conversational Question Answering. Experimental results with state-of-the-art (SOTA) neural architectures demonstrate that: 1) large language models ( LLMs) show poor performances in this subjective topic. 2) Context understanding is a crucial factor in building benign human-machine interaction. 3) Current models defect in the application of pragmatic reasoning. As a result, we call on more attention to improve the ability of context understanding, reasoning and implied meaning modeling.