Search Results for author:
Found 11 papers, 9 papers with code
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:
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.
Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics
Recently, researchers have employed MT metrics for various new use cases, such as data filtering and translation re-ranking.
Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!
Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according to their correlation with human judgments.
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications.
Ranked #1 on
Entity Linking
on N3-RSS-500
Document-level Closed Information Extraction
Entity Linking
+1
Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends
Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks.
Ranked #1 on
Coreference Resolution
on WikiCoref
The Effect of Training Schedules on Morphological Robustness and Generalization
In this paper, we define various training schedules to specify how these variations are introduced during an evolutionary learning process.
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:
