The insights gained from Attention-Constrained Inference (ACI) in the context of decoder-only text-to-speech models can be extended to other sequence-to-sequence tasks like machine translation or language modeling by leveraging similar principles of alignment constraints. In machine translation, for instance, ensuring a proper alignment between source and target sequences is crucial for accurate translation. By identifying Alignment-Emerged Attention Maps (AEAMs) in the decoder's self-attention mechanism and applying constraining masks based on these alignments, one can guide the model to generate translations in a more monotonic and accurate manner. This approach can help mitigate issues like word skipping, repeating, or misalignment, similar to those encountered in text-to-speech tasks.
Furthermore, in language modeling, maintaining coherence and fluency in generated text is essential. By adapting the ACI methodology to language modeling tasks, one could potentially improve the model's ability to generate text that follows a logical flow and maintains proper context. By identifying internal representations related to alignment within the model and applying constraints during inference, it is possible to enhance the robustness and quality of generated sequences in various sequence-to-sequence tasks beyond text-to-speech.