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Multi-modal Context Modelling for Machine Translation

Objective

Automatically translating human language has been a long sought-after goal in the field of Natural Language Processing (NLP). Machine Translation (MT) can significantly lower communication barriers, with enormous potential for positive social and economic impact. The dominant paradigm is Statistical Machine Translation (SMT), which learns to translate from human-translated examples.

Human translators have access to a number of contextual cues beyond the actual segment to translate when performing translation, for example images associated with the text and related documents. SMT systems, however, completely disregard any form of non-textual context and make little or no reference to wider surrounding textual content. This results in translations that miss relevant information or convey incorrect meaning. Such issues drastically affect reading comprehension and may make translations useless. This is especially critical for user-generated content such as social media posts -- which are often short and contain non-standard language -- but applies to a wide range of text types.

The novel and ambitious idea in this proposal is to devise methods and algorithms to exploit global multi-modal information for context modelling in SMT. This will require a significantly disruptive approach with new ways to acquire multilingual multi-modal representations, and new machine learning and inference algorithms that can process rich context models. The focus will be on three context types: global textual content from the document and related texts, visual cues from images and metadata including topic, date, author, source. As test beds, two challenging user-generated datasets will be used: Twitter posts and product reviews.

This highly interdisciplinary research proposal draws expertise from NLP, Computer Vision and Machine Learning and claims that appropriate modelling of multi-modal context is key to achieve a new breakthrough in SMT, regardless of language pair and text type.

Host institution

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Net EU contribution
€ 1 010 513,67
Address
South Kensington Campus Exhibition Road
SW7 2AZ London
United Kingdom

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Region
London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
Non-EU contribution
€ 0,00

Beneficiaries (2)

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
United Kingdom
Net EU contribution
€ 1 010 513,67
Address
South Kensington Campus Exhibition Road
SW7 2AZ London

See on map

Region
London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
Non-EU contribution
€ 0,00
THE UNIVERSITY OF SHEFFIELD

Participation ended

United Kingdom
Net EU contribution
€ 483 257,33
Address
Firth Court Western Bank
S10 2TN Sheffield

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Region
Yorkshire and the Humber South Yorkshire Sheffield
Activity type
Higher or Secondary Education Establishments
Non-EU contribution
€ 0,00