Title: Attentional Context Alignment for Multimodal Sequential Learning. Abstract: Transformer-based methods have gone mainstream in multimodal sequential learning. The intra and inter modality interactions are captured by the query-key associations of multi-head attention, where the calculated multimodal contexts are expected to be relevant to the query modality. However, in existing literature, the alignments between different calculated contextual sequences, that can back-evaluate the effectiveness of multi-head attention, are under-explored. Based on this concern, we propose a new constrained scheme called Multimodal Contextual Contrast (MCC), which could align the attentional sequences from both local and global perspectives, making the attentional capture more accurate. Concretely, the multimodal contexts of different modalities are mapped into a common feature space, those contexts at the same sequential step are considered as a positive group and the remaining sets are negative. From local perspective, we sample the negative groups for a positive group by randomly changing the sequential step of one specific context and keeping the other stay the same. From coarse global perspective, we divide all the contextual groups into two sets (i.e., aligned and unaligned), making the total score of aligned group relatively large. We extend the vectorial inner product operation for more input and calculate the aligned score for each multimodal group. Considering that the computational complexity scales exponentially to the number of modalities, we adopt stochastic expectation approximation (SEA) for the real process. The extensive experimental results on several tasks reveal the effectiveness of our contributions. 