RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER
Authors: Lin Sun, Jiquan Wang, Kai Zhang, Yindu Su, Fangsheng Weng13860-13868
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we deeply analyze the changes in visual attention before and after the use of text-image relation propagation. Our model achieves state-of-the-art performance on the MNER datasets. Experiments Datasets In the experiments, we use three datasets to evaluate the performance. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, Zhejiang University City College, Hangzhou, China 2 College of Computer Science and Technology, Zhejiang University, Hangzhou, China 3 Department of Computer Science and Technology, Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Multitask training procedure of Rp BERT for MNER. Input: The TRC dataset and MNER dataset. Output: θRp BERT , θRes Net, θF Cs, θbi LST M, and θCRF . |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | TRC dataset of Bloomberg LP (Vempala and Preot iuc-Pietro 2019) MNER dataset of Fudan University (Zhang et al. 2018) MNER dataset of Snap Research (Lu et al. 2018) |
| Dataset Splits | Yes | The authors labeled 8,257 tweet texts using the BIO2 tagging scheme and used a 4,000/1,000/3,257 train/dev/test split. The authors labeled 6,882 tweet texts using the BIO tagging scheme and used a 4,817/1,032/1,033 train/dev/test split. We follow the same split of 8:2 for train/test sets as in (Vempala and Preot iuc-Pietro 2019). |
| Hardware Specification | No | The paper mentions models like ResNet-152 and BERT-Base/BERT-Large but does not specify any hardware details such as GPU or CPU models, memory, or cloud computing instances used for experiments. |
| Software Dependencies | No | The paper mentions tools and models like "fast Text Crawl", "Res Net-152", "BERT-base-uncased model", "Adam", "bi LSTM-CRF", and "Viterbi algorithm", but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Table 2: Hyperparameters of the Rp BERT and bi LSTM-CRF models. Hyperparameter Value LSTM hidden state size 256 +Rp BERT 1024 LSTM layer 2 mini-batch size 8 char embedding dimension 25 optimizer Adam learning rate 1e-4 learning rate for finetuning Rp BERT and Res Net 1e-6 dropout rate 0.5 |