  Named entity recognition (NER) is a fundamental task in natural language
processing that involves identifying and classifying entities in sentences into
pre-defined types. It plays a crucial role in various research fields,
including entity linking, question answering, and online product
recommendation. Recent studies have shown that incorporating multilingual and
multimodal datasets can enhance the effectiveness of NER. This is due to
language transfer learning and the presence of shared implicit features across
different modalities. However, the lack of a dataset that combines
multilingualism and multimodality has hindered research exploring the
combination of these two aspects, as multimodality can help NER in multiple
languages simultaneously. In this paper, we aim to address a more challenging
task: multilingual and multimodal named entity recognition (MMNER), considering
its potential value and influence. Specifically, we construct a large-scale
MMNER dataset with four languages (English, French, German and Spanish) and two
modalities (text and image). To tackle this challenging MMNER task on the
dataset, we introduce a new model called 2M-NER, which aligns the text and
image representations using contrastive learning and integrates a multimodal
collaboration module to effectively depict the interactions between the two
modalities. Extensive experimental results demonstrate that our model achieves
the highest F1 score in multilingual and multimodal NER tasks compared to some
comparative and representative baselines. Additionally, in a challenging
analysis, we discovered that sentence-level alignment interferes a lot with NER
models, indicating the higher level of difficulty in our dataset.
