  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings
that frequently arise in real-life conversations and is essential for the
development of communicative social agents. In this paper, we introduce a novel
challenge, DiPlomat, aiming at benchmarking machines' capabilities on 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 cohesive framework towards general
pragmatic understanding. Our dataset is created through the utilization of
Amazon Mechanical Turk ( AMT ), resulting in a total of 4, 177 multi-turn
dialogues. In conjunction with the dataset, we propose two tasks, Pragmatic
Identification and Reasoning (PIR) and Conversational Question Answering (CQA).
Experimental results with state-of-the-art (SOTA) neural architectures reveal
several significant findings: 1) large language models ( LLMs) exhibit poor
performance in tackling this subjective domain; 2) comprehensive comprehension
of context emerges as a critical factor for establishing benign human-machine
interactions; 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.
