Papers by Julia Ive
deepQuest: A Framework for Neural-based Quality Estimation (C18-1)
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| Challenge: | Predicting Machine Translation (MT) quality has been limited to word and sentence-level prediction. |
| Approach: | They propose a framework that can generalize neural QE approaches to the level of documents. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on document-level quality estimates and is 40 times faster to train. |
A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality? (2020.lrec-1)
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Julia Ive, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Joachim Van den Bogaert, Eduardo Farah, Christine Maroti, Artur Ventura, Maxim Khalilov
| Challenge: | Current state-of-the-art in Neural Machine Translation (NMT) has reached remarkable progress, but human evaluations are often judged as having lower quality than top NMT systems. |
| Approach: | They propose to use a machine translation dataset with post-edited high-quality neural machine translation and independent human references to compare the results. |
| Outcome: | The proposed dataset includes 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, and a post-edited version of such translation by professional translator. |
Simultaneous Machine Translation with Visual Context (2020.emnlp-main)
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| Challenge: | Simultaneous machine translation (SiMT) aims to reproduce human interpretation, where an interpreter translates spoken utterances as they are produced. |
| Approach: | They propose to add visual context to siMT to compensate for the missing source context . they show visual-grounded models are much better than commonly used global features . |
| Outcome: | The proposed models reach up to 3 BLEU points improvement under low latency scenarios. |
Deep Copycat Networks for Text-to-Text Generation (D19-1)
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| Challenge: | Text-to-text generation tasks require copying words from the input to the output. |
| Approach: | They propose a transformer-based pointer network for text-to-text generation which generates more abstractive summaries and a further extension of this architecture for automatic post-editing. |
| Outcome: | The proposed model outperforms existing models in text-to-text generation tasks and improves translation accuracy. |
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path. |
| Approach: | They propose a multimodal approach to simultaneous machine translation using reinforcement learning with strategies to integrate visual and textual information in both the agent and the environment. |
| Outcome: | The proposed multimodal approach improves translation quality while keeping latency low while providing visual cues. |
Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection (2024.naacl-srw)
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| Challenge: | Existing methods for code clone detection are fast and scalable, but they may fail to detect clones that have different syntax or structure. |
| Approach: | They propose to use graph-based methods for code clone detection instead of sequence-based models. |
| Outcome: | The proposed graph-based methods outperforms sequence-based models on cross-lingual code clones. |
Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study (2024.lrec-main)
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| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
| Approach: | They collect first dataset of textual posts by same users before and after being diagnosed with depression and build multiple predictive models based on Transformers and BERT. |
| Outcome: | The proposed model can be used to detect depression and suicidal thoughts in users who are not diagnosed with depression or suicide. |
SURF: Semantic-level Unsupervised Reward Function for Machine Translation (2022.naacl-main)
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| Challenge: | Reinforcement Learning (RL) is dependent on the reward formulation due to the intrinsic difficulty of the task in the high-dimensional discrete action space and the sparseness of the standard reward functions. |
| Approach: | They propose a maximally dense semantic-level unsupervised reward function which mimics human evaluation by considering both sentence fluency and semantic similarity. |
| Outcome: | The proposed reward outperforms the standard sparse reward by 2% on average for in- and out-of-domain settings. |
Exploring Transformer Text Generation for Medical Dataset Augmentation (2020.lrec-1)
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| Challenge: | Natural Language Processing (NLP) is a powerful tool to unlock the vast troves of unstructured data in clinical text. |
| Approach: | They propose a method for augmenting unstructured patient information to allow NLP model development on downstream clinically relevant tasks. |
| Outcome: | The proposed method beats baselines on a downstream classification task and can be used for NLP model development. |
Exploring Supervised and Unsupervised Rewards in Machine Translation (2021.eacl-main)
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| Challenge: | Autoregressive sequence-to-sequence (seq2sequ) neural architectures have become the de facto approach in Machine Translation (MT). |
| Approach: | They propose to make models less reliant on cross-entropy loss and evaluation metrics . they propose an entropicity-regularised RL method that explores the action space . |
| Outcome: | The proposed method exploits the action space and unsupervised reward function to balance between exploration and exploitation. |
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case (2021.emnlp-main)
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Jingqing Zhang, Luis Bolanos Trujillo, Tong Li, Ashwani Tanwar, Guilherme Freire, Xian Yang, Julia Ive, Vibhor Gupta, Yike Guo
| Challenge: | Contextualised word embeddings are powerful tool to detect contextual synonyms, but most of the current SOTA methods are supervised and underexploit the potential of the context. |
| Approach: | They propose a self-supervised approach which detects contextual synonyms of concepts being trained on the data created by shallow matching. |
| Outcome: | The proposed approach outperforms the previous SOTA with gains of up to 4.5 and 4.0 absolute points after fine-tuning with as little as 20% of the labelled data. |
Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media (2024.findings-acl)
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| Challenge: | Existing studies have shown that social media users' posts can help identify depression, bipolar disorder or self-harm. |
| Approach: | They propose a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media timelines. |
| Outcome: | The proposed approach produces clinically meaningful summaries from social media user timelines, suitable for mental health monitoring. |
Distilling Translations with Visual Awareness (P19-1)
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| Challenge: | Existing 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. |
| Approach: | They propose a translate-and-refine approach to multimodal machine translation where images are only used by a second stage decoder to generate a good first draft translation and to improve over this draft. |
| Outcome: | The proposed approach generates a good translation and improves over the draft by making better use of the target language textual context and making use of visual context. |