596 citations · 901 across the 38 of their papers we have counts for
12 papers · 1 filter
Multimodal Machine Translation through Visuals and Speech
Umut Sulubacak, Ozan Caglayan, Stig-Arne Grönroos +4
Multimodal machine translation involves drawing information from more than one modality, based on the assumption that the additional modalities will contain useful alternative view…
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…
Imperial College London Submission to VATEX Video Captioning Task
Ozan Caglayan, Zixiu Wu, Pranava Madhyastha +2
This paper describes the Imperial College London team's submission to the 2019' VATEX video captioning challenge, where we first explore two sequence-to-sequence models, namely a r…
Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality
Carolina Scarton, Mikel L. Forcada, Miquel Esplà-Gomis +1
Devising metrics to assess translation quality has always been at the core of machine translation (MT) research. Traditional automatic reference-based metrics, such as BLEU, have s…
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation
Zhenhao Li, Lucia Specia
Neural Machine Translation (NMT) models have been proved strong when translating clean texts, but they are very sensitive to noise in the input. Improving NMT models robustness can…
EASSE: Easier Automatic Sentence Simplification Evaluation
Fernando Alva-Manchego, Louis Martin, Carolina Scarton +1
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single acces…
Phrase Localization Without Paired Training Examples
Josiah Wang, Lucia Specia
Localizing phrases in images is an important part of image understanding and can be useful in many applications that require mappings between textual and visual information. Existi…
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…
VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions
Pranava Madhyastha, Josiah Wang, Lucia Specia
We address the task of evaluating image description generation systems. We propose a novel image-aware metric for this task: VIFIDEL. It estimates the faithfulness of a generated c…
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…
Probing the Need for Visual Context in Multimodal Machine Translation
Ozan Caglayan, Pranava Madhyastha, Lucia Specia +1
Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial. We posit that this is a consequence…