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Found 27 papers, 6 papers with code
Information Extraction from Legal Documents: A Study in the Context of Common Law Court Judgements
‘Common Law’ judicial systems follow the doctrine of precedent, which means the legal principles articulated in court judgements are binding in subsequent cases in lower courts.
Evaluating Hierarchical Document Categorisation
Hierarchical document categorisation is a special case of multi-label document categorisation, where there is a taxonomic hierarchy among the labels.
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.
Modelling Emotion Dynamics in Song Lyrics with State Space Models
In this work, we propose a method to predict emotion dynamics in song lyrics without song-level supervision.
Generating Diverse Descriptions from Semantic Graphs
Text generation from semantic graphs is traditionally performed with deterministic methods, which generate a unique description given an input graph.
On the (In)Effectiveness of Images for Text Classification
Images are core components of multi-modal learning in natural language processing (NLP), and results have varied substantially as to whether images improve NLP tasks or not.
BOSS: Bayesian Optimization over String Spaces
This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops.
Modelling Uncertainty in Collaborative Document Quality Assessment
In the context of document quality assessment, previous work has mainly focused on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task.
On the Role of Scene Graphs in Image Captioning
Scene graphs represent semantic information in images, which can help image captioning system to produce more descriptive outputs versus using only the image as context.
Neural Speech Translation using Lattice Transformations and Graph Networks
Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation.
A Unified Neural Architecture for Instrumental Audio Tasks
Within Music Information Retrieval (MIR), prominent tasks -- including pitch-tracking, source-separation, super-resolution, and synthesis -- typically call for specialised methods, despite their similarities.
Graph-to-Sequence Learning using Gated Graph Neural Networks
Many NLP applications can be framed as a graph-to-sequence learning problem.
Learning Kernels over Strings using Gaussian Processes
Non-contiguous word sequences are widely known to be important in modelling natural language.
Modelling Representation Noise in Emotion Analysis using Gaussian Processes
Emotion Analysis is the task of modelling latent emotions present in natural language.
Exploring Prediction Uncertainty in Machine Translation Quality Estimation
Machine Translation Quality Estimation is a notoriously difficult task, which lessens its usefulness in real-world translation environments.
Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
We address the problem of automatically finding the parameters of a statistical machine translation system that maximize BLEU scores while ensuring that decoding speed exceeds a minimum value.
Learning Structural Kernels for Natural Language Processing
Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing.
