Work

AMR-to-text Generation with Synchronous Node Replacement Grammar

Year: 2017

Type: conference-paper

Abstract: This paper addresses the task of AMR-totext generation by leveraging synchronous node replacement grammar.During training, graph-to-string rules are learned using a heuristic extraction algorithm.At test time, a graph transducer is applied to collapse input AMRs and generate output sentences.Evaluated on a standard benchmark, our method gives the state-of-the-art result.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p17-2002

Authors: Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea

Institutions: IBM (United States), Singapore University of Technology and Design, IBM Research - Thomas J. Watson Research Center, University of Rochester

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Ministry of Education, India

OpenAlex ID: W2964035651