Generating Text from Anonymised Structures

International audienceSurface realisation maps a meaning representation (MR) to a text, usually a single sentence. In this paper, we introduce a new parallel dataset of deep meaning representations and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model's ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation

Generating Text from Anonymised Structures

International audienceSurface realisation maps a meaning representation (MR) to a text, usually a single sentence. In this paper, we introduce a new parallel dataset of deep meaning representations and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model's ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation