  Most of the existing compositional generalization datasets are
synthetically-generated, resulting in a lack of natural language variation.
While there have been recent attempts to introduce non-synthetic datasets for
compositional generalization, they suffer from either limited data scale or a
lack of diversity in the forms of combinations. To better investigate
compositional generalization with more linguistic phenomena and compositional
diversity, we propose the DIsh NamE Recognition (DiNeR) task and create a large
realistic Chinese dataset. Given a recipe instruction, models are required to
recognize the dish name composed of diverse combinations of food, actions, and
flavors. Our dataset consists of 3,811 dishes and 228,114 recipes, and involves
plenty of linguistic phenomena such as anaphora, omission and ambiguity. We
provide two strong baselines based on T5 and large language models (LLMs). This
work contributes a challenging task, baseline methods to tackle the task, and
insights into compositional generalization in the context of dish name
recognition. Code and data are available at https://github.com/Jumpy-pku/DiNeR.
