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Found 21 papers, 1 papers with code
An End-to-End Approach for Full Bridging Resolution
We demonstrate the performance of an end-to-end transformer-based higher-order coreference model finetuned for the task of full bridging.
Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution
Specifically, we convert the external knowledge source (in this case, WordNet) into a graph, and learn embeddings of the graph nodes of low dimension to capture the crucial features of the graph topology and, at the same time, rich semantic information.
Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning
Graph-based semi-supervised learning is appealing when labels are scarce but large amounts of unlabeled data are available.
A Tale of Two Laws of Semantic Change: Predicting Synonym Changes with Distributional Semantic Models
Another related question is whether and how lexical relations over pairs of words, such as synonymy, change over time.
Fair Without Leveling Down: A New Intersectional Fairness Definition
In this work, we consider the problem of intersectional group fairness in the classification setting, where the objective is to learn discrimination-free models in the presence of several intersecting sensitive groups.
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks
Recent work on predicting category structure with distributional models, using either static word embeddings (Heyman and Heyman, 2019) or contextualized language models (CLMs) (Misra et al., 2021), report low correlations with human ratings, thus calling into question their plausibility as models of human semantic memory.
Fair NLP Models with Differentially Private Text Encoders
Encoded text representations often capture sensitive attributes about individuals (e. g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups.
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing
This paper presents a simple framework for characterizing morphological complexity and how it encodes syntactic information.
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images
Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data.
Delexicalized Word Embeddings for Cross-lingual Dependency Parsing
This paper presents a new approach to the problem of cross-lingual dependency parsing, aiming at leveraging training data from different source languages to learn a parser in a target language.
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm
This paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm.
French TimeBank: An ISO-TimeML Annotated Reference Corpus
This article presents the main points in the creation of the French TimeBank (Bittar, 2010), a reference corpus annotated according to the ISO-TimeML standard for temporal annotation.
