Search Results for author:
Found 34 papers, 18 papers with code
UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation
This paper describes the participation of the UoB-NLP team in the ProfNER-ST shared subtask 7a.
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features
This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text.
CogNLP-Sheffield at CMCL 2021 Shared Task: Blending Cognitively Inspired Features with Transformer-based Language Models for Predicting Eye Tracking Patterns
The CogNLP-Sheffield submissions to the CMCL 2021 Shared Task examine the value of a variety of cognitively and linguistically inspired features for predicting eye tracking patterns, as both standalone model inputs and as supplements to contextual word embeddings (XLNet).
Pre-Trained Language Models Represent Some Geographic Populations Better Than Others
This paper measures the skew in how well two families of LLMs represent diverse geographic populations.
Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text
Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages.
Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation
Domain experts across engineering, healthcare, and education follow strict standards for producing quality content such as technical manuals, medication instructions, and children's reading materials.
Word Boundary Information Isn't Useful for Encoder Language Models
As such, removing these symbols has been shown to have a beneficial effect on the processing of morphologically complex words for transformer encoders in the pretrain-finetune paradigm.
Flesch or Fumble? Evaluating Readability Standard Alignment of Instruction-Tuned Language Models
Readability metrics and standards such as Flesch Kincaid Grade Level (FKGL) and the Common European Framework of Reference for Languages (CEFR) exist to guide teachers and educators to properly assess the complexity of educational materials before administering them for classroom use.
Are Emergent Abilities in Large Language Models just In-Context Learning?
Large language models have exhibited emergent abilities, demonstrating exceptional performance across diverse tasks for which they were not explicitly trained, including those that require complex reasoning abilities.
Construction Grammar and Language Models
In this chapter, we explore three distinct approaches to the interplay between computational methods and Construction Grammar: (i) computational methods for text analysis, (ii) computational Construction Grammar, and (iii) deep learning models, with a particular focus on language models.
Effective Cross-Task Transfer Learning for Explainable Natural Language Inference with T5
We compare sequential fine-tuning with a model for multi-task learning in the context where we are interested in boosting performance on two tasks, one of which depends on the other.
Abstraction not Memory: BERT and the English Article System
Article prediction is a task that has long defied accurate linguistic description.
Sample Efficient Approaches for Idiomaticity Detection
In particular we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings, on the task of idiomaticity detection.
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context.
Uniform Complexity for Text Generation
Large language models (LLMs) have shown promising results in a wide array of generative NLP tasks, such as summarization and machine translation.
Improving Tokenisation by Alternative Treatment of Spaces
We find that our modified algorithms lead to improved performance on downstream NLP tasks that involve handling complex words, whilst having no detrimental effect on performance in general natural language understanding tasks.
Learned Construction Grammars Converge Across Registers Given Increased Exposure
These simulations are repeated with increasing amounts of exposure, from 100k to 2 million words, to measure the impact of exposure on the convergence of grammars.
UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction
Toxicity is pervasive in social media and poses a major threat to the health of online communities.
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models
Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms.
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.
This paper describes our submission to SemEval 2021 Task 2.
CxGBERT: BERT meets Construction Grammar
BERT's training objectives give it access to a tremendous amount of lexico-semantic information, and while BERTology has shown that BERT captures certain important linguistic dimensions, there have been no studies exploring the extent to which BERT might have access to constructional information.
UoB at SemEval-2020 Task 1: Automatic Identification of Novel Word Senses
Much as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users.
Incorporating Count-Based Features into Pre-Trained Models for Improved Stance Detection
The explosive growth and popularity of Social Media has revolutionised the way we communicate and collaborate.
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets -- RoBERTa Ensembles and The Continued Relevance of Handcrafted Features
This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text.
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information
Pre-trained language model word representation, such as BERT, have been extremely successful in several Natural Language Processing tasks significantly improving on the state-of-the-art.
Augmenting Neural Metaphor Detection with Concreteness
The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while.
CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysis
The growing popularity and applications of sentiment analysis of social media posts has naturally led to sentiment analysis of posts written in multiple languages, a practice known as code-switching.
Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data
The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed.
Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences
In this paper we present the Widrow-Hoff rule and its applications to language data.
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data
The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed.
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams
Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately.
Integrating Question Classification and Deep Learning for improved Answer Selection
We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection.
High Accuracy Rule-based Question Classification using Question Syntax and Semantics
We present in this paper a purely rule-based system for Question Classification which we divide into two parts: The first is the extraction of relevant words from a question by use of its structure, and the second is the classification of questions based on rules that associate these words to Concepts.
Ranked #1 on
Text Classification
on TREC-50
