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State-of-the-art natural language processing models have been shown toachieve remarkable performance in 'closed-world' settings where all the labelsin the evaluation set are known at training time. However, in real-worldsettings, 'novel' instances that do not belong to any known class are oftenobserved. This renders the ability to deal with novelties crucial. To initiatea systematic research in this important area of 'dealing with novelties', weintroduce 'NoveltyTask', a multi-stage task to evaluate a system's performanceon pipelined novelty 'detection' and 'accommodation' tasks. We providemathematical formulation of NoveltyTask and instantiate it with the authorshipattribution task that pertains to identifying the correct author of a giventext. We use Amazon reviews corpus and compile a large dataset (consisting of250k instances across 200 authors/labels) for NoveltyTask. We conductcomprehensive experiments and explore several baseline methods for the task.Our results show that the methods achieve considerably low performance makingthe task challenging and leaving sufficient room for improvement. Finally, webelieve our work will encourage research in this underexplored area of dealingwith novelties, an important step en route to developing robust systems.

A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution