Update README.md

README.md

@@ -1,3 +1,82 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# M-ABSA
+
+This repo contains the data and code for our paper ****M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis****.
+
+[![arXiv](https://img.shields.io/badge/arXiv-2502.11824-b31b1b.svg)](https://arxiv.org/abs/2502.11824)
+
+
+# Data Description:
+
+All datasets are stored in the data/ folder:
+
+- All dataset contains 7 domains. 
+
+ ```
+domains = ["coursera", "hotel", "laptop", "restaurant", "phone", "sight", "food"]
+``` 
+- Each dataset contains 21 languages.
+```
+langs = ["ar", "da", "de", "en", "es", "fr", "hi", "hr", "id", "ja", "ko", "nl", "pt", "ru", "sk", "sv", "sw", "th", "tr", "vi", "zh"]
+```
+- Each dataset is divided into training, validation, and test sets. Each sentence is separated by __"####"__, with the first part being the sentence and the second part being the corresponding triplet. Here is an example, where the triplet includes __[aspect, category, sentiment]__.
+
+```
+This coffee brews up a nice medium roast with exotic floral and berry notes .####[['coffee', 'food quality', 'positive', 'nice']]
+```
+
+## Requirements
+
+We recommend you to install the specified version of the following packages:
+
+- transformers==4.0.0
+-  sentencepiece==0.1.91
+-  pytorch_lightning==0.8.1
+
+## Quick Start for the Baseline
+
+- Set up the environment as described in the above section.
+- Download the pre-trained mT5-base model from [https://huggingface.co/google/mt5-base](https://huggingface.co/google/mt5-base) and place it under the folder mT5-base/ .
+- Run command bash run.sh, which train the model on source language under UABSA/TASD task.
+- Run command bash evaluate.sh, which test the model on target language under UABSA/TASD task.
+
+****Detailed Usage****
+
+We conduct experiments on two ABSA subtasks with M-ABSA dataset in the paper, you can change the parameters in run.sh to try them:
+
+```
+python main.py --task tasd \
+            --dataset hotel \
+            --model_name_or_path mt5-base \
+            --paradigm extraction \
+            --n_gpu 0 \
+            --do_train \
+            --do_direct_eval \
+            --train_batch_size 16 \
+            --gradient_accumulation_steps 2 \
+            --eval_batch_size 16 \
+            --learning_rate 3e-4 \
+            --num_train_epochs 5
+```
+
+- $task refers to one of the ABSA task in [uabsa, tasd]
+- $dataset refers to one of the seven datasets in [food, restaurant, coursera, laptop, sight, phone, hotel]
+
+
+## Citation
+
+If the code or dataset is used in your research, please star our repo and cite our paper as follows:
+```
+@misc{wu2025mabsa,
+      title={M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis}, 
+      author={Chengyan Wu and Bolei Ma and Yihong Liu and Zheyu Zhang and Ningyuan Deng and Yanshu Li and Baolan Chen and Yi Zhang and Barbara Plank and Yun Xue},
+      year={2025},
+      eprint={2502.11824},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.11824}, 
+}
+```
+