Summary of 2m-ner: Contrastive Learning For Multilingual and Multimodal Ner with Language and Modal Fusion, by Dongsheng Wang et al.
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion
by Dongsheng Wang, Xiaoqin Feng, Zeming Liu, Chuan Wang
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to named entity recognition (NER) is proposed, which combines multilingualism and multimodality to identify and classify entities across various languages and modalities. This task is crucial for applications like entity linking, question answering, and online product recommendation. Recent studies have shown that incorporating multimodal datasets can enhance NER’s effectiveness by leveraging shared implicit features. However, the lack of a dataset combining multilingualism and multimodality has hindered research in this area. To address this challenge, a large-scale MMNER dataset is constructed with four languages (English, French, German, and Spanish) and two modalities (text and image). A new model called 2M-NER is introduced, which aligns text and image representations using contrastive learning and integrates a multimodal collaboration module to depict interactions between the modalities. Experimental results demonstrate that the proposed model achieves the highest F1 score in multilingual and multimodal NER tasks compared to baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of recognizing named entities is being explored, which involves combining multiple languages and types of data like text and images. This research has many potential applications, such as helping computers understand what’s being said in different languages or recommending products based on what people are saying about them. The challenge here is that there isn’t a big dataset that combines all these things, so it’s hard to train machines to do this task well. To fix this, the researchers created a large dataset with four languages and two types of data. They then developed a new way of analyzing this data, called 2M-NER, which works better than other approaches. |
Keywords
» Artificial intelligence » Entity linking » F1 score » Named entity recognition » Ner » Question answering
