Embedding and Clustering Multi-Entity Sequences

Core to much of modern deep learning is the notion of representation learning, learning representations of things that are useful for performing some task(s) related to those things. Encoder-only language models, for example, learn representations of language useful for performing language-related tasks, often classification. While fruitful in many applications, inherent is the assumption that only one classification is to be made for a particular input. This poses challenges when multiple classifications are to be made about different portions of a single record, such as emotion recognition in conversation (ERC) where the objective is to classify the emotion in each utterance of a dialog. Existing methods for this task typically either involve redundant computation, non-trivial post-processing outside of the core language model backbone, or both. To address this, we generalize recent work for deriving player-specific embeddings from multi-player sequences of events in sport for domain-agnostic application while also enabling it to leverage inter-entity relationships. Seeing the efficacy of the method in regression and classification tasks, we explore how it can be used to cluster player representations, proposing a novel approach for distribution-aware deep-clustering in the absence of labels. We demonstrate how the proposed methods yield state-of-the-art performance on the disparate tasks of ERC in Natural Language Processing (NLP), long-tail partial-label-learning (LT-PLL) in Computer Vision (CV), and player form clustering in sports analytics.