Search Results for author: Julia Ive

Found 32 papers, 8 papers with code

SURF: Semantic-level Unsupervised Reward Function for Machine Translation

no code implementations NAACL 2022 Atijit Anuchitanukul, Julia Ive

The performance of Reinforcement Learning (RL) for natural language tasks including Machine Translation (MT) is crucially dependent on the reward formulation.

Machine Translation Reinforcement Learning (RL) +4

Safe Training with Sensitive In-domain Data: Leveraging Data Fragmentation To Mitigate Linkage Attacks

no code implementations30 Apr 2024 Mariia Ignashina, Julia Ive

Current text generation models are trained using real data which can potentially contain sensitive information, such as confidential patient information and the like.

Text Generation

Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media

no code implementations29 Jan 2024 Jiayu Song, Jenny Chim, Adam Tsakalidis, Julia Ive, Dana Atzil-Slonim, Maria Liakata

We introduce a hybrid abstractive summarisation approach combining hierarchical VAE with LLMs (LlaMA-2) to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring.

Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection

no code implementations27 Dec 2023 Mohammed Ataaur Rahaman, Julia Ive

To the best of our knowledge, this is the first work to demonstrate the superiority of graph-based methods over sequence-based methods on cross-lingual code clone detection.

Clone Detection

Controlled Text Generation using T5 based Encoder-Decoder Soft Prompt Tuning and Analysis of the Utility of Generated Text in AI

no code implementations6 Dec 2022 Damith Chamalke Senadeera, Julia Ive

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.

Decoder Text Generation

Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing

no code implementations29 May 2022 Hongshu Liu, Nabeel Seedat, Julia Ive

Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision making in healthcare contexts.

Decision Making Gaussian Processes +1

Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint

no code implementations24 May 2022 Heng-Yi Wu, Jingqing Zhang, Julia Ive, Tong Li, Vibhor Gupta, Bingyuan Chen, Yike Guo

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.

Data Augmentation Table-to-Text Generation +1

Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge

no code implementations19 Apr 2022 Ashwani Tanwar, Jingqing Zhang, Julia Ive, Vibhor Gupta, Yike Guo

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.

Word Embeddings

Revisiting Contextual Toxicity Detection in Conversations

no code implementations24 Nov 2021 Atijit Anuchitanukul, Julia Ive, Lucia Specia

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.

Data Augmentation Toxic Comment Classification

Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases

no code implementations24 Jul 2021 Jingqing Zhang, Luis Bolanos, Ashwani Tanwar, Julia Ive, Vibhor Gupta, Yike Guo

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

1 code implementation EACL 2021 Julia Ive, Andy Mingren Li, Yishu Miao, Ozan Caglayan, Pranava Madhyastha, Lucia Specia

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.

Machine Translation reinforcement-learning +2

Exploring Supervised and Unsupervised Rewards in Machine Translation

1 code implementation EACL 2021 Julia Ive, Zixu Wang, Marina Fomicheva, Lucia Specia

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.

Machine Translation Reinforcement Learning (RL) +2

Simultaneous Machine Translation with Visual Context

1 code implementation EMNLP 2020 Ozan Caglayan, Julia Ive, Veneta Haralampieva, Pranava Madhyastha, Loïc Barrault, Lucia Specia

Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible.

Machine Translation Translation

Exploring Transformer Text Generation for Medical Dataset Augmentation

2 code implementations LREC 2020 Ali Amin-Nejad, Julia Ive, Sumithra Velupillai

Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research.

Synthetic Data Generation Text Generation

Deep Copycat Networks for Text-to-Text Generation

1 code implementation IJCNLP 2019 Julia Ive, Pranava Madhyastha, Lucia Specia

Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output.

Automatic Post-Editing Text Generation +2

Transformer-based Cascaded Multimodal Speech Translation

no code implementations EMNLP (IWSLT) 2019 Zixiu Wu, Ozan Caglayan, Julia Ive, Josiah Wang, Lucia Specia

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

no code implementations5 Aug 2019 Zixiu Wu, Julia Ive, Josiah Wang, Pranava Madhyastha, Lucia Specia

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.


Distilling Translations with Visual Awareness

1 code implementation ACL 2019 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 textual context is not sufficient.

Ranked #3 on Multimodal Machine Translation on Multi30K (Meteor (EN-FR) metric)

Decoder Multimodal Machine Translation +1

deepQuest: A Framework for Neural-based Quality Estimation

2 code implementations COLING 2018 Julia Ive, Fr{\'e}d{\'e}ric Blain, Lucia Specia

Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks.

Feature Engineering Machine Translation +2

Parallel Sentence Compression

no code implementations COLING 2016 Julia Ive, Fran{\c{c}}ois Yvon

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.

Machine Translation Semantic Role Labeling +3

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