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Deriving Entity-Specific Embeddings from Multi-Entity Sequences

Underpinning much of the recent progress in deep learning is the transformer architecture, which takes as input a sequence of embeddings E and emits an updated sequence of embeddings E’. A special [CLS] embedding is often included in this sequence, serving as a description of the sequence once processed and used as the basis for subsequent sequence-level tasks. The processed [CLS] embedding loses utility, however, when the model is presented with a multi-entity sequence and asked to perform an entity-specific task. When processing a multi-speaker dialogue, for example, the [CLS] embedding describes the entire dialogue, not any individual utterance/speaker. Existing methods toward entity-specific prediction involve redundant computation or post-processing outside of the transformer. We present a novel methodology for deriving entity-specific embeddings from a multi-entity sequence completely within the transformer, with a loose definition of entity amenable to many problem spaces. To show the generic applicability of our method, we apply it to widely different tasks: emotion recognition in conversation and player performance projection in baseball and show that it can be used to achieve SOTA in both. Code can be found at https://github.com/c-heat16/EntitySpecificEmbeddings.