29 citations · 145 across the 26 of their papers we have counts for
27 papers
Vision-Language Models Struggle to Align Entities across Modalities
Iñigo Alonso, Gorka Azkune, Ander Salaberria +2
Cross-modal entity linking refers to the ability to align entities and their attributes across different modalities. While cross-modal entity linking is a fundamental skill needed…
EuskañolDS: A Naturally Sourced Corpus for Basque-Spanish Code-Switching
Maite Heredia, Jeremy Barnes, Aitor Soroa
Code-switching (CS) remains a significant challenge in Natural Language Processing (NLP), mainly due a lack of relevant data. In the context of the contact between the Basque and S…
HiTZ at VarDial 2025 NorSID: Overcoming Data Scarcity with Language Transfer and Automatic Data Annotation
Jaione Bengoetxea, Mikel Zubillaga, Ekhi Azurmendi +4
In this paper we present our submission for the NorSID Shared Task as part of the 2025 VarDial Workshop (Scherrer et al., 2025), consisting of three tasks: Intent Detection, Slot F…
XNLIeu: a dataset for cross-lingual NLI in Basque
Maite Heredia, Julen Etxaniz, Muitze Zulaika +3
XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. In this paper…
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts
Patrick Bareiß, Roman Klinger, Jeremy Barnes
Emotion classification in text is a challenging task due to the processes involved when interpreting a textual description of a potential emotion stimulus. In addition, the set of…
Annotating Norwegian Language Varieties on Twitter for Part-of-Speech
Petter Mæhlum, Andre Kåsen, Samia Touileb +1
Norwegian Twitter data poses an interesting challenge for Natural Language Processing (NLP) tasks. These texts are difficult for models trained on standardized text in one of the t…
Direct parsing to sentiment graphs
David Samuel, Jeremy Barnes, Robin Kurtz +3
This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance th…
The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus
Samia Touileb, Jeremy Barnes
Recent years have seen a rise in interest for cross-lingual transfer between languages with similar typology, and between languages of various scripts. However, the interplay betwe…
Structured Sentiment Analysis as Dependency Graph Parsing
Jeremy Barnes, Robin Kurtz, Stephan Oepen +2
Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e,g,, target extr…
skweak: Weak Supervision Made Easy for NLP
Pierre Lison, Jeremy Barnes, Aliaksandr Hubin
We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks. Weak supervision is an emerging machin…
Large-Scale Contextualised Language Modelling for Norwegian
Andrey Kutuzov, Jeremy Barnes, Erik Velldal +2
We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), includ…
NorDial: A Preliminary Corpus of Written Norwegian Dialect Use
Jeremy Barnes, Petter Mæhlum, Samia Touileb
Norway has a large amount of dialectal variation, as well as a general tolerance to its use in the public sphere. There are, however, few available resources to study this variatio…
If you've got it, flaunt it: Making the most of fine-grained sentiment annotations
Jeremy Barnes, Lilja Øvrelid, Erik Velldal
Fine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by th…
Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification
Andrew Moore, Jeremy Barnes
The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results. Within this paper we show that these models are not r…
Named Entity Recognition without Labelled Data: A Weak Supervision Approach
Pierre Lison, Aliaksandr Hubin, Jeremy Barnes +1
Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data i…
Cross-lingual Emotion Intensity Prediction
Irean Navas Alejo, Toni Badia, Jeremy Barnes
Emotion intensity prediction determines the degree or intensity of an emotion that the author expresses in a text, extending previous categorical approaches to emotion detection. W…
A Systematic Comparison of Architectures for Document-Level Sentiment Classification
Jeremy Barnes, Vinit Ravishankar, Lilja Øvrelid +1
Documents are composed of smaller pieces - paragraphs, sentences, and tokens - that have complex relationships between one another. Sentiment classification models that take into a…
A Fine-Grained Sentiment Dataset for Norwegian
Lilja Øvrelid, Petter Mæhlum, Jeremy Barnes +1
We introduce NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. The underlying tex…
Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study
Jeremy Barnes, Roman Klinger
Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., Englis…
LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in Spanish
Jeremy Barnes
This paper details LTG-Oslo team's participation in the sentiment track of the NEGES 2019 evaluation campaign. We participated in the task with a hierarchical multi-task network, w…
On the Effect of Word Order on Cross-lingual Sentiment Analysis
Àlex R. Atrio, Toni Badia, Jeremy Barnes
Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent ne…
Sentiment analysis is not solved! Assessing and probing sentiment classification
Jeremy Barnes, Lilja Øvrelid, Erik Velldal
Neural methods for SA have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative diff…
Improving Sentiment Analysis with Multi-task Learning of Negation
Jeremy Barnes, Erik Velldal, Lilja Øvrelid
Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most preva…
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Domain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to th…