Senseattention

Senseattention is an approach in natural language processing that integrates word sense disambiguation with attention mechanisms to produce sense-aware representations of text. The central idea is to treat each token as potentially associated with multiple semantic senses and to learn an attention distribution over these senses conditioned on the surrounding context. The resulting sense-aware representations aim to improve tasks that depend on precise meaning, such as disambiguation, retrieval, and reasoning.

In typical implementations, a token appears with a set of candidate senses drawn from a sense inventory or a learned multi-sense embedding space. A context encoder (such as a transformer or recurrent network) builds contextual features for the token, and a sense attention module computes weights over senses by comparing sense embeddings to the context. The final representation combines the sense embeddings weighted by the attention scores, which guides downstream models for tagging, parsing, or prediction.

Training can be supervised, using labeled sense annotations, or weakly supervised via auxiliary objectives (e.g., language modeling with sense penalties) and self-training. Some approaches jointly optimize disambiguation and the target task, allowing the model to refine sense embeddings through backpropagation.

Applications include improving machine translation, information retrieval, question answering, and semantic parsing, especially in domains with polysemous terms or specialized vocabularies. Senseattention can also complement contextualized word representations by adding explicit sense distinctions.

Related concepts include word sense disambiguation, multi-sense embeddings, and attention mechanisms. Challenges involve constructing comprehensive sense inventories, computational overhead, and evaluation across varied languages and domains. As a research direction, senseattention aims to blend symbolic sense information with neural context modeling to enhance semantic understanding.