Search Results for author:
Found 17 papers, 11 papers with code
Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network
Inflectional morphology has since long been a useful testing ground for broader questions about generalisation in language and the viability of neural network models as cognitive models of language.
Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks
Memorisation is a natural part of learning from real-world data: neural models pick up on atypical input-output combinations and store those training examples in their parameter space.
Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge
Our empirical findings show that the accuracy of UniMorph Labeller is 98%, and that, in all language models studied (including ALBERT, BERT, RoBERTa, and DeBERTa), alien tokenization leads to poorer generalizations compared to morphological tokenization for semantic compositionality of word meanings.
Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study
We challenge hate speech models via new train-test splits of existing datasets that rely on the clustering of models' hidden representations.
Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation
When training a neural network, it will quickly memorise some source-target mappings from your dataset but never learn some others.
Non-Compositionality in Sentiment: New Data and Analyses
When natural language phrases are combined, their meaning is often more than the sum of their parts.
Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality
We illustrate that comparing data's representations in models with and without the bottleneck can be used to produce a compositionality metric.
State-of-the-art generalisation research in NLP: A taxonomy and review
We present a taxonomy for characterising and understanding generalisation research in NLP.
Text Characterization Toolkit
In NLP, models are usually evaluated by reporting single-number performance scores on a number of readily available benchmarks, without much deeper analysis.
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation
In this work, we investigate whether the non-compositionality of idioms is reflected in the mechanics of the dominant NMT model, Transformer, by analysing the hidden states and attention patterns for models with English as source language and one of seven European languages as target language.
The paradox of the compositionality of natural language: a neural machine translation case study
Obtaining human-like performance in NLP is often argued to require compositional generalisation.
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks.
The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse
There has been an increased interest in modelling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinformation detection, among others.
Being neighbourly: Neural metaphor identification in discourse
Existing approaches to metaphor processing typically rely on local features, such as immediate lexico-syntactic contexts or information within a given sentence.
Modelling the interplay of metaphor and emotion through multitask learning
Metaphors allow us to convey emotion by connecting physical experiences and abstract concepts.
Compositionality decomposed: how do neural networks generalise?
Despite a multitude of empirical studies, little consensus exists on whether neural networks are able to generalise compositionally, a controversy that, in part, stems from a lack of agreement about what it means for a neural model to be compositional.
Transcoding compositionally: using attention to find more generalizable solutions
While sequence-to-sequence models have shown remarkable generalization power across several natural language tasks, their construct of solutions are argued to be less compositional than human-like generalization.
