Generating Text in Multiple Languages from Multiple Sources – XNLG

Natural Language Generation (NLG) produces text from data, text or meaning representations.
With the boom of AI and deep learning technology, the field of NLG has been growing at exponential speed. While NLG has many potential applications (summarization, data verbalisation, text simplification, robo-journalism, story telling, etc.), key research questions are still outstanding such as how to handle the lack of training data and how to allow for NLG into the many natural languages. Using state-of-the-art neural technologies (BERT language modelling, Encoder-Decoder architectures, multi-task and transfer learning), XNLG will (i) investigate techniques to compensate for the lack of training data and (ii) develop models for multi-lingual, multi-source generation i.e., generation into multiple languages and from either meaning representations (MR2T), text (T2T) or data (D2T). We will in particular investigate whether a single meaning representation (MR) can be used as input for generation into multiple languages and how it compares with generation from language dependent MRs; how well the models we’ll propose for MR2T generation extend to D2T and T2T generation; and whether MRs provide a better basis for MR2T generation than the powerful continuous representations currently created for sentences by neural models such as BERT and ELMO.