  We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition
covering 33 entity classes across 12 languages, in both monolingual and
multilingual settings. This dataset aims to tackle the following practical
challenges in NER: (i) effective handling of fine-grained classes that include
complex entities like movie titles, and (ii) performance degradation due to
noise generated from typing mistakes or OCR errors. The dataset is compiled
from open resources like Wikipedia and Wikidata, and is publicly available.
Evaluation based on the XLM-RoBERTa baseline highlights the unique challenges
posed by MULTICONER V2: (i) the fine-grained taxonomy is challenging, where the
scores are low with macro-F1=0.63 (across all languages), and (ii) the
corruption strategy significantly impairs performance, with entity corruption
resulting in 9% lower performance relative to non-entity corruptions across all
languages. This highlights the greater impact of entity noise in contrast to
context noise.
