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Found 6 papers, 5 papers with code
ExtEnD: Extractive Entity Disambiguation
Local models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pre-trained language models.
ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension
Supervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient.
Ranked #1 on
Word Sense Disambiguation
on Supervised:
Code-Switching with Word Senses for Pretraining in Neural Machine Translation
Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022).
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments.
Entity Disambiguation with Entity Definitions
Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions.
ESC: Redesigning WSD with Extractive Sense Comprehension
By means of an extensive array of experiments, we show that ESC unleashes the full potential of our model, leading it to outdo all of its competitors and to set a new state of the art on the English WSD task.
Ranked #4 on
Word Sense Disambiguation
on Supervised:
