8 citations · 19 across the 12 of their papers we have counts for
13 papers · 1 filter
Controlled Text Generation using T5 based Encoder-Decoder Soft Prompt Tuning and Analysis of the Utility of Generated Text in AI
Damith Chamalke Senadeera, Julia Ive
Controlled text generation is a very important task in the arena of natural language processing due to its promising applications. In order to achieve this task we mainly introduce…
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint
Heng-Yi Wu, Jingqing Zhang, Julia Ive +4
Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drasticall…
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge
Ashwani Tanwar, Jingqing Zhang, Julia Ive +2
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. However, reasoning with nu…
Revisiting Contextual Toxicity Detection in Conversations
Atijit Anuchitanukul, Julia Ive, Lucia Specia
Understanding toxicity in user conversations is undoubtedly an important problem. Addressing "covert" or implicit cases of toxicity is particularly hard and requires context. Very…
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
Jingqing Zhang, Luis Bolanos, Tong Li +6
Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remai…
Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases
Jingqing Zhang, Luis Bolanos, Ashwani Tanwar +3
Objective: Clinical notes contain information not present elsewhere, including drug response and symptoms, all of which are highly important when predicting key outcomes in acute c…
Exploring Supervised and Unsupervised Rewards in Machine Translation
Julia Ive, Zixu Wang, Marina Fomicheva +1
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.…
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation
Julia Ive, Andy Mingren Li, Yishu Miao +3
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…
Simultaneous Machine Translation with Visual Context
Ozan Caglayan, Julia Ive, Veneta Haralampieva +3
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation…
Transformer-based Cascaded Multimodal Speech Translation
Zixiu Wu, Ozan Caglayan, Julia Ive +2
This paper describes the cascaded multimodal speech translation systems developed by Imperial College London for the IWSLT 2019 evaluation campaign. The architecture consists of an…
Predicting Actions to Help Predict Translations
Zixiu Wu, Julia Ive, Josiah Wang +2
We address the task of text translation on the How2 dataset using a state of the art transformer-based multimodal approach. The question we ask ourselves is whether visual features…
Is artificial data useful for biomedical Natural Language Processing algorithms?
Zixu Wang, Julia Ive, Sumithra Velupillai +1
A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical…
Distilling Translations with Visual Awareness
Julia Ive, Pranava Madhyastha, Lucia Specia
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 te…