2019
EMNLP
EMNLP 2019
University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task
Abstract
AbstractThe University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.
🌉
Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
🧭
Keyword Pioneer
— multilingual nlg
🐣
Hot Topic Early Bird
— document-level translation
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio