Add link to paper

README.md

@@ -12,6 +12,8 @@ AS-SRL is the first Chinese speech-based Semantic Role Labeling (SRL) dataset, c
 
 This dataset was developed to address the growing need for speech-based language understanding capabilities, particularly for SRL from speech input directly. It enables research on end-to-end approaches for SRL from speech, which can help overcome limitations of traditional pipeline methods (ASR followed by text-based SRL) such as error propagation and loss of useful acoustic features.
 
+It was proposed in the paper [Semantic Role Labeling: A Systematical Survey](https://huggingface.co/papers/2502.08660).
+
 ## Features
 
 - 9,000 authentic Mandarin speech recordings with corresponding transcripts and SRL annotations
@@ -23,10 +25,10 @@ This dataset was developed to address the growing need for speech-based language
 
 If you use this dataset, please cite:
 
-    @inproceedings{chen-etal-2024-semantic,
-    title = "Semantic Role Labeling from Chinese Speech via End-to-End Learning",
-    author = "Chen, Huiyao and Li, Xinxin and Zhang, Meishan and Zhang, Min",
-    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
-    year = "2024",
-    pages = "8898--8911"
-    }
+@inproceedings{chen-etal-2024-semantic,
+title = "Semantic Role Labeling from Chinese Speech via End-to-End Learning",
+author = "Chen, Huiyao and Li, Xinxin and Zhang, Meishan and Zhang, Min",
+booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
+year = "2024",
+pages = "8898--8911"
+}