1. You should compare your model with more recent models [1-5].
2. Contrastive learning has been widely used in Intent Detection [6-9], although the tasks are not identical. I think the novelty of this simple modification is not suitable for EMNLP.
3. You should provide more details about the formula in the text, e.g. $\ell_{BCE}$ ,even if it is simple, give specific details.
4. You don't provide the value of some hyper-parameters, such as τ.
5. The Figure 1 is blurry, which affects reading.
[1] Qin L, Wei F, Xie T, et al. GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021: 178-188.
[2] Xing B, Tsang I. Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022: 159-169.
[3] Xing B, Tsang I. Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022: 3964-3975.
[4] Song M, Yu B, Quangang L, et al. Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022: 7967-7977.
[5] Cheng L, Yang W, Jia W. A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding[J]. arXiv e-prints, 2022: arXiv: 2211.12220.
[6] Liu H, Zhang F, Zhang X, et al. An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling[C]//Findings of the Association for Computational Linguistics: EMNLP 2021. 2021: 1945-1955.
[7] Qin L, Chen Q, Xie T, et al. GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022: 2677-2686.
[8] Liang S, Shou L, Pei J, et al. Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022: 9903-9918.
[9] Chang Y H, Chen Y N. Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding[J]. arXiv preprint arXiv:2205.00693, 2022.
3. You should provide more details about the formula in the text, e.g. $\ell_{BCE}$ ,even if it is simple, give specific details.