## 2 Related Works
### Multimodal Fusion for Emotion Recognition  
[PERSON] et al. [12] and [PERSON] et al. [13] employed encoder-decoder architectures in emotion recognition systems. [PERSON] et al. applied a self-attention transformer as the encoder for multimodal learning, whereas [PERSON] et al. employed a cross-modal transformer to encode multimodal embeddings. Both methods incorporated emotion-level embeddings into the decoders with multimodal sequences, effectively aligning labels and modalities. These approaches improved performance. However, the multimodal encoder [12] directly concatenates sequences in the temporal dimension, increasing the sequence length and computational costs for the transformer. Direct concatenation may yield suboptimal results due to differences in distribution, length, and noise in sequences from the three modalities.  
Studies have explored cross-modal interaction by incorporating cross-attention mechanisms from transformers. [PERSON] et al. [14] and [PERSON] et al. [15] employed six transformers to establish directional pairwise cross-modal attention in the vision, language, and audio modalities. They augmented these with three additional transformers for self-attention mechanisms. [PERSON] et al. [16] introduced a cascade multihead attention module, connecting cross-attention modules in series to mine interactions sequentially in the three modalities. This module was extended three times, corresponding to the permutations of the fusing order of the modalities. Despite their effectiveness, these methods incur high computational costs due to the bidirectional or sequential cross-attention transformers.  
In addition to treating all modalities equivalently, some studies have proposed text-centered frameworks. For example, [PERSON] et al. [1] introduced the fully end-to-end (FE2E) and multimodal end-to-end sparse models, which extract features and learn to recognize human emotions using a universal deep learning model. The multimodel end-to-end sparse model integrates cross-modal attention layers into audio and visual conditional neural networks, where these layers operate over audio or visual feature maps using query text vectors.  
Additionally, [PERSON] et al. [2] applied global text embedding to enhance semantic representations of audio and visual feature sequences. They fused audio and visual sequences using a cross-modal encoder [17], a variant of the MBT [8], employing a cross-transformer instead of a self-attention transformer. Further, [PERSON] et al. [18] initially fused audio and visual feature sequences into a higher-level space using the MBT, then merged this bimodal feature sequence with the textual feature sequences. Although these studies applied the MBT to learn audiovisual clues, they did not explore its application to textual data. In contrast, the current work proposes a homogeneous fusion version of MBT called XMBT to learn pairwise cross-modal interactions between all modalities. Most existing transformer-based fusion methods either suffer from high computational costs due to bidirectional attention or require a predefined fusion order, which limits flexibility. The proposed XMBT enhances fusion efficiency by introducing bottleneck tokens, enabling more effective cross-modal learning while maintaining computational efficiency. Unlike prior methods, XMBT learns simultaneous cross-modal interactions across all modalities rather than relying on predefined hierarchies.