Search Results for author:
Found 28 papers, 12 papers with code
‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models
This paper studies whether state-of-the-art, pre-trained language models are capable of passing moral judgments on posts retrieved from a popular Reddit user board.
Incorporating Zoning Information into Argument Mining from Biomedical Literature
Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers.
Learning to Generate and Evaluate Fact-checking Explanations with Transformers
By introducing human-centred evaluation methods and developing specialised datasets, we emphasise the need for aligning Artificial Intelligence (AI)-generated explanations with human judgements.
Representation Learning of Structured Data for Medical Foundation Models
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare.
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
State-of-the-art Large Language Models (LLMs) are accredited with an increasing number of different capabilities, ranging from reading comprehension, over advanced mathematical and reasoning skills to possessing scientific knowledge.
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues
Experimental results show that LLMs can effectively model ASR noise, and incorporating this noisy data into the training process significantly improves the robustness and accuracy of medical dialogue summarization systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document summarisation.
uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization
Current methods often sacrifice key information for faithfulness or introduce confabulations when prioritizing informativeness.
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension
Performance of NLP systems is typically evaluated by collecting a large-scale dataset by means of crowd-sourcing to train a data-driven model and evaluate it on a held-out portion of the data.
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering
There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare.
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation
In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning for automated evaluation of resulting essays, based on human feedback along argument quality dimensions (Task 4 SQE).
Automated Clinical Coding for Outpatient Departments
Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records.
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records
This paper describes PULSAR, our system submission at the ImageClef 2023 MediQA-Sum task on summarising patient-doctor dialogues into clinical records.
PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models
Medical progress notes play a crucial role in documenting a patient's hospital journey, including his or her condition, treatment plan, and any updates for healthcare providers.
A Two-Stage Decoder for Efficient ICD Coding
Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures.
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation
Furthermore, evaluating key points is crucial in ensuring that the automatically generated summaries are useful.
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond
In this paper, we provide an overview of the different methods for sentence representation learning, focusing mostly on deep learning models.
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification
Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures.
Ranked #1 on
Medical Code Prediction
on MIMIC-IV-ICD10-top50
Towards Human-Centred Explainability Benchmarks For Text Classification
Progress on many Natural Language Processing (NLP) tasks, such as text classification, is driven by objective, reproducible and scalable evaluation via publicly available benchmarks.
Can Transformers Reason in Fragments of Natural Language?
State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts.
RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour
Each news article was annotated for two aspects: (1) indicators of forced labour as classification labels and (2) snippets of the text that justify labelling decisions.
Multi Label Text Classification
Multi-Label Text Classification
+1
WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals.
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?
We define seven MRC skills which require the understanding of different discourse relations.
Machine Reading Comprehension
Natural Language Understanding
Semantics Altering Modifications for Evaluating Comprehension in Machine Reading
Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans.
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models
Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data.
A Framework for Evaluation of Machine Reading Comprehension Gold Standards
Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text.
DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings
This paper describes DBee, a database to support the construction of data-intensive AI applications.
Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text.
