Search Results for author:
Found 12 papers, 6 papers with code
(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection
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 research problems.
Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms
We apply our method to the highly challenging ANLI dataset, where our framework improves the performance of both a DeBERTa-base and BERT baseline.
Computational modeling of semantic change
In this chapter we provide an overview of computational modeling for semantic change using large and semi-large textual corpora.
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models
We can further improve model performance and span-level decisions by using the e-SNLI explanations during training.
DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages
Word meaning is notoriously difficult to capture, both synchronically and diachronically.
Challenges for Computational Lexical Semantic Change
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 and linguistics.
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
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 linguistics.
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes.
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures
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 of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces.
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment.
Coming to Your Senses: on Controls and Evaluation Sets in Polysemy Research
This and additional results point to the conclusion that performance gains as reported in previous work may be an artifact of random sense assignment, which is equivalent to sub-sampling and multiple estimation of word vector representations.
Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models
This article evaluates three proposed laws of semantic change.
