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Found 18 papers, 4 papers with code
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings
Word embeddings have advanced the state of the art in NLP across numerous tasks.
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?
We furthermore provide evidence through similarity measures and investigation of parameters that this lack of positive influence is due to output separability -- which we argue is of use for machine translation but detrimental elsewhere.
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled.
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate.
Isotropy, Clusters, and Classifiers
Whether embedding spaces use all their dimensions equally, i. e., whether they are isotropic, has been a recent subject of discussion.
Grounded and Well-rounded: A Methodological Approach to the Study of Cross-modal and Cross-lingual Grounding
Grounding has been argued to be a crucial component towards the development of more complete and truly semantically competent artificial intelligence systems.
Why bother with geometry? On the relevance of linear decompositions of Transformer embeddings
A recent body of work has demonstrated that Transformer embeddings can be linearly decomposed into well-defined sums of factors, that can in turn be related to specific network inputs or components.
"Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics
Definition Modeling, the task of generating definitions, was first proposed as a means to evaluate the semantic quality of word embeddings-a coherent lexical semantic representations of a word in context should contain all the information necessary to generate its definition.
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces
Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm.
Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings
Word embeddings have advanced the state of the art in NLP across numerous tasks.
A Game Interface to Study Semantic Grounding in Text-Based Models
Can language models learn grounded representations from text distribution alone?
What Meaning-Form Correlation Has to Compose With
Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive.
What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language
Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive.
G\'en\'eration automatique de d\'efinitions pour le fran\ccais (Definition Modeling in French)
La g{\'e}n{\'e}ration de d{\'e}finitions est une t{\^a}che r{\'e}cente qui vise {\`a} produire des d{\'e}finitions lexicographiques {\`a} partir de plongements lexicaux.
Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations.
