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Found 29 papers, 8 papers with code
Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach
To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.
SURF: Semantic-level Unsupervised Reward Function for Machine Translation
The performance of Reinforcement Learning (RL) for natural language tasks including Machine Translation (MT) is crucially dependent on the reward formulation.
Findings of the Third Workshop on Automatic Simultaneous Translation
This paper reports the results of the shared task we hosted on the Third Workshop of Automatic Simultaneous Translation (AutoSimTrans).
Predicting Moments of Mood Changes Overtime from Imbalanced Social Media Data
The second is predicting the degree of suicide risk as a user-level classification task.
Controlled Text Generation using T5 based Encoder-Decoder Soft Prompt Tuning and Analysis of the Utility of Generated Text in AI
In order to achieve this task we mainly introduce the novel soft prompt tuning method of using soft prompts at both encoder and decoder levels together in a T5 model and investigate the performance as the behaviour of an additional soft prompt related to the decoder of a T5 model in controlled text generation remained unexplored.
Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing
Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision making in healthcare contexts.
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint
Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drastically reduce manual efforts to condense this data into reports.
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge
Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases.
Revisiting Contextual Toxicity Detection in Conversations
We then propose to bring these findings into computational detection models by introducing and evaluating (a) neural architectures for contextual toxicity detection that are aware of the conversational structure, and (b) data augmentation strategies that can help model contextual toxicity detection.
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
Contextualised word embeddings is a powerful tool to detect contextual synonyms.
Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases
We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU).
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation
This paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation quality and low latency; and (b) visual information to support this process by providing additional (visual) contextual information which may be available before the textual input is produced.
Exploring Supervised and Unsupervised Rewards in Machine Translation
Reinforcement Learning (RL) is a powerful framework to address the discrepancy between loss functions used during training and the final evaluation metrics to be used at test time.
Simultaneous Machine Translation with Visual Context
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible.
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?
We introduce a machine translation dataset for three pairs of languages in the legal domain with post-edited high-quality neural machine translation and independent human references.
Exploring Transformer Text Generation for Medical Dataset Augmentation
Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research.
Deep Copycat Networks for Text-to-Text Generation
Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output.
Transformer-based Cascaded Multimodal Speech Translation
Upon conducting extensive experiments, we found that (i) the explored visual integration schemes often harm the translation performance for the transformer and additive deliberation, but considerably improve the cascade deliberation; (ii) the transformer and cascade deliberation integrate the visual modality better than the additive deliberation, as shown by the incongruence analysis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Predicting Actions to Help Predict Translations
The question we ask ourselves is whether visual features can support the translation process, in particular, given that this is a dataset extracted from videos, we focus on the translation of actions, which we believe are poorly captured in current static image-text datasets currently used for multimodal translation.
Is artificial data useful for biomedical Natural Language Processing algorithms?
A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility.
Distilling Translations with Visual Awareness
Previous work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient.
Ranked #3 on
Multimodal Machine Translation
on Multi30K
(Meteor (EN-FR) metric)
Sheffield Submissions for the WMT18 Quality Estimation Shared Task
In this paper we present the University of Sheffield submissions for the WMT18 Quality Estimation shared task.
deepQuest: A Framework for Neural-based Quality Estimation
Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks.
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health
In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health.
Parallel Sentence Compression
In this paper, we study ways to extend sentence compression in a bilingual context, where the goal is to obtain parallel compressions of parallel sentences.
