  Previous entity disambiguation (ED) methods adopt a discriminative paradigm,
where prediction is made based on matching scores between mention context and
candidate entities using length-limited encoders. However, these methods often
struggle to capture explicit discourse-level dependencies, resulting in
incoherent predictions at the abstract level (e.g. topic or category). We
propose CoherentED, an ED system equipped with novel designs aimed at enhancing
the coherence of entity predictions. Our method first introduces an
unsupervised variational autoencoder (VAE) to extract latent topic vectors of
context sentences. This approach not only allows the encoder to handle longer
documents more effectively, conserves valuable input space, but also keeps a
topic-level coherence. Additionally, we incorporate an external category
memory, enabling the system to retrieve relevant categories for undecided
mentions. By employing step-by-step entity decisions, this design facilitates
the modeling of entity-entity interactions, thereby maintaining maximum
coherence at the category level. We achieve new state-of-the-art results on
popular ED benchmarks, with an average improvement of 1.3 F1 points. Our model
demonstrates particularly outstanding performance on challenging long-text
scenarios.
