Two MNER Datasets and Codes for our ACL'2020 paper: Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer

Author

Jianfei Yu

jfyu@njust.edu.cn

July 1, 2020

• The preprocessed CoNLL format files are provided in this repo. For each tweet, the first line is its image id, and the following lines are its textual contents.
• Step 1：Download each tweet's associated images via this link (https://drive.google.com/file/d/1PpvvncnQkgDNeBMKVgG2zFYuRhbL873g/view)
• Step 2: Change the image path in line 552 and line 554 of the "run_mtmner_crf.py" file
• Step 3: Download the pre-trained ResNet-152 via this link (https://download.pytorch.org/models/resnet152-b121ed2d.pth)
• Setp 4: Put the pre-trained ResNet-152 model under the folder named "resnet"

• PyTorch 1.0.0
• Python 3.7
• pytorch-crf 0.7.2

• This is the training code of tuning parameters on the dev set, and testing on the test set. Note that you can change "CUDA_VISIBLE_DEVICES=2" based on your available GPUs.

sh run_mtmner_crf.sh

• We show our running logs on twitter-2015 and twitter-2017 in the folder "log files". Note that the results are a little bit lower than the results reported in our paper, since the experiments were run on different servers.

• In our codes, we mainly use "seqeval" to compute Micro-F1 as the evaluation metrics. Note that if you use the latest version of seqeval (as it may also report the weighted F1 score), you may need to change our Micro-F1 score parsing code as follows: float(report.split('\n')[-3].split(' ')[-2].split(' ')[-1]) to float(report.split('\n')[-4].split(' ')[-2].split(' ')[-1]).
• In addition to "seqeval", we also borrow the evaluation code from this repo to compute Micro-F1. The Micro-F1 scores based on these two codes should be the same.

• Using these two datasets means you have read and accepted the copyrights set by Twitter and dataset providers.
• Most of the codes are based on the codes provided by huggingface: https://github.com/huggingface/transformers.