Update README.md

README.md

@@ -47,3 +47,53 @@ cd ~
 git clone https://github.com/scutcyr/dstc11-simmc2.1-iflytek.git
 ```
 (3) Follow the [README](https://github.com/scutcyr/dstc11-simmc2.1-iflytek#readme) to use the model.
+
+
+## References
+
+```
+@inproceedings{kottur-etal-2021-simmc,
+    title = "{SIMMC} 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations",
+    author = "Kottur, Satwik  and
+      Moon, Seungwhan  and
+      Geramifard, Alborz  and
+      Damavandi, Babak",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.401",
+    doi = "10.18653/v1/2021.emnlp-main.401",
+    pages = "4903--4912",
+}
+
+@inproceedings{lee-etal-2022-learning,
+    title = "Learning to Embed Multi-Modal Contexts for Situated Conversational Agents",
+    author = "Lee, Haeju  and
+      Kwon, Oh Joon  and
+      Choi, Yunseon  and
+      Park, Minho  and
+      Han, Ran  and
+      Kim, Yoonhyung  and
+      Kim, Jinhyeon  and
+      Lee, Youngjune  and
+      Shin, Haebin  and
+      Lee, Kangwook  and
+      Kim, Kee-Eung",
+    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-naacl.61",
+    doi = "10.18653/v1/2022.findings-naacl.61",
+    pages = "813--830",
+}
+```
+
+
+## Acknowledge
+* We would like to express our gratitude to the authors of [Hugging Face's Transformers🤗](https://huggingface.co/) and its open source community for the excellent design on pretrained models usage.
+* We would like to express our gratitude to [Meta Research | Facebook AI Research](https://github.com/facebookresearch) for the SIMMC2.1 dataset and the baseline code.
+* We would like to express our gratitude to [KAIST-AILab](https://github.com/KAIST-AILab/DSTC10-SIMMC) for the basic research framework on SIMMC2.0.