Search Results for author:
Found 78 papers, 23 papers with code
Investigating Language Relationships in Multilingual Sentence Encoders Through the Lens of Linguistic Typology
The results provide insight into their information-sharing mechanisms and suggest that these linguistic properties are encoded jointly across typologically similar languages in these models.
Proactive Gradient Conflict Mitigation in Multi-Task Learning: A Sparse Training Perspective
Advancing towards generalist agents necessitates the concurrent processing of multiple tasks using a unified model, thereby underscoring the growing significance of simultaneous model training on multiple downstream tasks.
Cross-modal Information Flow in Multimodal Large Language Models
While there exists a variety of studies investigating the processing of linguistic information within large language models, little is currently known about the inner working mechanism of MLLMs and how linguistic and visual information interact within these models.
Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalisation of Misinformation Detection Models
This paper introduces misinfo-general, a benchmark dataset for evaluating misinformation models' ability to perform out-of-distribution generalisation.
Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning
Improving the alignment of Large Language Models (LLMs) with respect to the cultural values that they encode has become an increasingly important topic.
Density Matrices for Metaphor Understanding
In physics, density matrices are used to represent mixed states, i. e. probabilistic mixtures of pure states.
Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning
Our results show that even a minimal ablation of induction heads leads to ICL performance decreases of up to ~32% for abstract pattern recognition tasks, bringing the performance close to random.
A framework for annotating and modelling intentions behind metaphor use
Metaphors are part of everyday language and shape the way in which we conceptualize the world.
On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations?
While multilingual language models (MLMs) have been trained on 100+ languages, they are typically only evaluated across a handful of them due to a lack of available test data in most languages.
Are LLMs classical or nonmonotonic reasoners? Lessons from generics
Recent scholarship on reasoning in LLMs has supplied evidence of impressive performance and flexible adaptation to machine generated or human feedback.
The Echoes of Multilinguality: Tracing Cultural Value Shifts during LM Fine-tuning
Texts written in different languages reflect different culturally-dependent beliefs of their writers.
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection
This mismatch can be partially attributed to the limitations of current evaluation setups that neglect the rapid evolution of online content and the underlying social graph.
Metaphor Understanding Challenge Dataset for LLMs
Metaphor understanding is therefore an essential task for large language models (LLMs).
Gradient-based Parameter Selection for Efficient Fine-Tuning
With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible.
Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks
Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing.
The language of prompting: What linguistic properties make a prompt successful?
The latest generation of LLMs can be prompted to achieve impressive zero-shot or few-shot performance in many NLP tasks.
Do large language models solve verbal analogies like children do?
We conclude that the LLMs we tested indeed tend to solve verbal analogies by association with C like children do.
Probing LLMs for Joint Encoding of Linguistic Categories
Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining.
How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning
We further study how different fine-tuning languages influence model performance on a given test language and find that they can both reinforce and complement the knowledge acquired from data of the test language itself.
What's the Meaning of Superhuman Performance in Today's NLU?
In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension.
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension
The task of multimodal referring expression comprehension (REC), aiming at localizing an image region described by a natural language expression, has recently received increasing attention within the research comminity.
FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs
We present FewShotTextGCN, a novel method designed to effectively utilize the properties of word-document graphs for improved learning in low-resource settings.
Scientific and Creative Analogies in Pretrained Language Models
This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2.
Data-Efficient Cross-Lingual Transfer with Language-Specific Subnetworks
Large multilingual language models typically share their parameters across all languages, which enables cross-lingual task transfer, but learning can also be hindered when training updates from different languages are in conflict.
Learning New Tasks from a Few Examples with Soft-Label Prototypes
Existing approaches to few-shot learning in NLP rely on large language models (LLMs) and/or fine-tuning of these to generalise on out-of-distribution data.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?
In this paper, we investigate what types of stereotypical information are captured by pretrained language models.
Ruddit: Norms of Offensiveness for English Reddit Comments
On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds.
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses.
Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective
Metaphor is an indispensable part of human cognition and everyday communication.
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.
How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection
Metaphors are widely used in political rhetoric as an effective framing device.
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability
Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language.
Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions
We set a baseline for two tasks related to populist attitudes and present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.
Ranked #1 on
Populist attitude
on Us Vs. Them
Multilingual and cross-lingual document classification: A meta-learning approach
The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods.
Cross-Lingual Document Classification
Document Classification
+2
A Multimodal Framework for the Detection of Hateful Memes
An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes.
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.
Cross-neutralising: Probing for joint encoding of linguistic information in multilingual models
Multilingual sentence encoders are widely used to transfer NLP models across languages.
What does it mean to be language-agnostic? Probing multilingual sentence encoders for typological properties
Multilingual sentence encoders have seen much success in cross-lingual model transfer for downstream NLP tasks.
Meta-Learning with Sparse Experience Replay for Lifelong Language Learning
Lifelong learning requires models that can continuously learn from sequential streams of data without suffering catastrophic forgetting due to shifts in data distributions.
Graph-based Modeling of Online Communities for Fake News Detection
Existing research has modeled the structure, style, content, and patterns in dissemination of online posts, as well as the demographic traits of users who interact with them.
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.
Joint Modelling of Emotion and Abusive Language Detection
The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online.
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples.
Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models
We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences.
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings
The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP).
Modelling the interplay of metaphor and emotion through multitask learning
Metaphors allow us to convey emotion by connecting physical experiences and abstract concepts.
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods
Abuse on the Internet represents an important societal problem of our time.
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models
Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings.
Deconstructing multimodality: visual properties and visual context in human semantic processing
Multimodal semantic models that extend linguistic representations with additional perceptual input have proved successful in a range of natural language processing (NLP) tasks.
CAMsterdam at SemEval-2019 Task 6: Neural and graph-based feature extraction for the identification of offensive tweets
We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offensive language identification in Twitter data.
Abusive Language Detection with Graph Convolutional Networks
Abuse on the Internet represents a significant societal problem of our time.
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification
Most current approaches to metaphor identification use restricted linguistic contexts, e. g. by considering only a verb's arguments or the sentence containing a phrase.
Author Profiling for Hate Speech Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet.
Neural Character-based Composition Models for Abuse Detection
The current state of the art approaches to abusive language detection, based on recurrent neural networks, do not explicitly address this problem and resort to a generic OOV (out of vocabulary) embedding for unseen words.
Author Profiling for Abuse Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet.
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Linguistic typology aims to capture structural and semantic variation across the world's languages.
A Report on the 2018 VUA Metaphor Detection Shared Task
As the community working on computational approaches to figurative language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area.
Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding.
Modelling semantic acquisition in second language learning
Using methods of statistical analysis, we investigate how semantic knowledge is acquired in English as a second language and evaluate the pace of development across a number of predicate types and content word combinations, as well as across the levels of language proficiency and native languages.
Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain
Research in computational semantics is increasingly guided by our understanding of human semantic processing.
Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions
Frame-semantic parsing and semantic role labelling, that aim to automatically assign semantic roles to arguments of verbs in a sentence, have become an active strand of research in NLP.
Modelling metaphor with attribute-based semantics
One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms.
Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning
We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings.
Psychologically Motivated Text Mining
Natural language processing techniques are increasingly applied to identify social trends and predict behavior based on large text collections.
Detecting Cross-Cultural Differences Using a Multilingual Topic Model
Understanding cross-cultural differences has important implications for world affairs and many aspects of the life of society.
