101 citations · 126 across the 16 of their papers we have counts for
19 papers
Crossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction
Yuxuan Liu, Haim Dubossarsky, Ruth Ahnert
Science fiction is widely credited with unsettling gender. This article asks whether that holds at the linguistic level, in how readily gendered reference gives way to its counterp…
Lexical Redefinition Reveals Context-Sensitive Instruction Following
Roksana Goworek, Haim Dubossarsky
How do language models reconcile conflicting contextual evidence within a prompt? We study this question using lexical redefinition, where an explicit instruction specifies how a w…
Rethinking Metrics for Lexical Semantic Change Detection
Roksana Goworek, Haim Dubossarsky
Lexical semantic change detection (LSCD) increasingly relies on contextualised language model embeddings, yet most approaches still quantify change using a small set of semantic ch…
Estranged Predictions: Measuring Semantic Category Disruption with Masked Language Modelling
Yuxuan Liu, Haim Dubossarsky, Ruth Ahnert
This paper examines how science fiction destabilises ontological categories by measuring conceptual permeability across the terms human, animal, and machine using masked language m…
Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Transfer in Sense-Aware Tasks
Roksana Goworek, Haim Dubossarsky
Cross-lingual transfer is central to modern NLP, enabling models to perform tasks in languages different from those they were trained on. A common assumption is that training on mo…
SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods
Roksana Goworek, Harpal Karlcut, Muhammad Shezad +10
This paper addresses the critical need for high-quality evaluation datasets in low-resource languages to advance cross-lingual transfer. While cross-lingual transfer offers a key s…
The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology
Aideen Fay, Inés García-Redondo, Qiquan Wang +2
Existing interpretability methods for Large Language Models (LLMs) predominantly capture linear directions or isolated features. This overlooks the high-dimensional, relational, an…
LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data
Naomi Baes, Raphaël Merx, Nick Haslam +2
Lexical Semantic Change (LSC) provides insight into cultural and social dynamics. Yet, the validity of methods for measuring different kinds of LSC remains unestablished due to the…
Analyzing Semantic Change through Lexical Replacements
Francesco Periti, Pierluigi Cassotti, Haim Dubossarsky +1
Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to w…
(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection
Francesco Periti, Haim Dubossarsky, Nina Tahmasebi
In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open researc…
Atomic Inference for NLI with Generated Facts as Atoms
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +2
With recent advances, neural models can achieve human-level performance on various natural language tasks. However, there are no guarantees that any explanations from these models…
Computational modeling of semantic change
Nina Tahmasebi, Haim Dubossarsky
In this chapter we provide an overview of computational modeling for semantic change using large and semi-large textual corpora. We aim to provide a key for the interpretation of r…
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +1
Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on in-distribution test sets. However, as these models ar…
DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages
Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen +2
Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized,…
Challenges for Computational Lexical Semantic Change
Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg +1
The computational study of lexical semantic change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences an…
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen +2
Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and li…
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Joe Stacey, Pasquale Minervini, Haim Dubossarsky +2
Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment cl…
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures
Haim Dubossarsky, Ivan Vulić, Roi Reichart +1
Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success…
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
Haim Dubossarsky, Simon Hengchen, Nina Tahmasebi +1
State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for…