Work

Underspecified Universal Dependency Structures as Inputs for Multilingual Surface Realisation

Year: 2018

Type: conference-paper

Abstract: In this paper, we present the datasets used in the Shallow and Deep Tracks of the First Multilingual Surface Realisation Shared Task (SR'18).For the Shallow Track, data in ten languages has been released: Arabic, Czech, Dutch, English, Finnish, French, Italian, Portuguese, Russian and Spanish.For the Deep Track, data in three languages is made available: English, French and Spanish.We describe in detail how the datasets were derived from the Universal Dependencies V2.0, and report on an evaluation of the Deep Track input quality.In addition, we examine the motivation for, and likely usefulness of, deriving NLG inputs from annotations in resources originally developed for Natural Language Understanding (NLU), and assess whether the resulting inputs supply enough information of the right kind for the final stage in the NLG process.

Source: International Conference on Natural Language Generation

DOI: 10.18653/v1/w18-6527

Authors: Simon Mille, Anja Belz, Bernd Bohnet, Leo Wanner

Institutions: Universitat Pompeu Fabra, University of Brighton, Google (United Kingdom), Institució Catalana de Recerca i Estudis Avançats

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, Horizon 2020 Framework Programme

Awards: Retrieval and Analysis of Heterogeneous Online Content for Terrorist Activity Recognition, Enhancing decision support and management services in extreme weather climate events, Visual and textual content re-purposing FOR(4) architecture, Design and video virtual reality games, G4937468798, G7208606672