Update README.md

README.md

@@ -16,6 +16,8 @@ tags:
 
 # SaleIntent-BERT
 
+**Our work has been acepted by EMNLP 2026**
+
 **SaleIntent-BERT** is a fine-tuned BERT classifier that reads a complete sales conversation and predicts **how the customer ended up** — from clear purchase intent down to hostility. It is the outcome-scoring half of the [SalesLLM benchmark](https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill).
 
 Scoring a sales dialogue with an LLM judge alone conflates two different questions: *did the salesperson run a good process?* and *did the customer actually want to buy at the end?* A model can be articulate, polite, and well-structured while the customer walks away — and an LLM judge, reading the whole transcript, tends to reward the articulate process. SaleIntent-BERT answers the second question independently, by looking only at where the conversation landed.
@@ -172,15 +174,18 @@ Accuracy is reported over the full 5-class problem. Note that the classes are no
 ## Citation
 
 ```bibtex
-@misc{salesllm,
-  title  = {SalesLLM: Benchmarking LLM Realistic Selling Skill},
-  author = {MultiSense},
-  year   = {2025},
-  url    = {https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill}
+@misc{su2026sellmoreplayless,
+      title={Sell More, Play Less: Benchmarking LLM Realistic Selling Skill}, 
+      author={Xuanbo Su and Wenhao Hu and Le Zhan and Yuting Xie and Kailin Lyu and Kaijie Chen and Ziwei Li and Yeqiang Wang and Haibo Su and Yunzhang Chen and Ling Huang},
+      year={2026},
+      eprint={2604.07054},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2604.07054}, 
 }
 ```
 
 ## Related
-
+- 📖 [Paper](https://arxiv.org/abs/2604.07054)
 - 📊 [SalesLLM benchmark & code](https://github.com/Bairong-Xdynamics/Benchmarking-LLM-Realistic-Selling-Skill)
 - 🤗 [CustomerLM](https://huggingface.co/MultiSense/CustomerLM) — the user simulator that generates the customer side of the dialogues this model scores