8 citations · 19 across the 12 of their papers we have counts for
5 papers · 1 filter
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…