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Neural Pipeline for Zero-Shot Data-to-Text Generation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10457007" target="_blank" >RIV/00216208:11320/22:10457007 - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.acl-long.271/" target="_blank" >https://aclanthology.org/2022.acl-long.271/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.18653/v1/2022.acl-long.271" target="_blank" >10.18653/v1/2022.acl-long.271</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Neural Pipeline for Zero-Shot Data-to-Text Generation

  • Original language description

    In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise. We examine how to avoid finetuning pretrained language models (PLMs) on D2T generation datasets while still taking advantage of surface realization capabilities of PLMs. Inspired by pipeline approaches, we propose to generate text by transforming single-item descriptions with a sequence of modules trained on general-domain text-based operations: ordering, aggregation, and paragraph compression. We train PLMs for performing these operations on a synthetic corpus WikiFluent which we build from English Wikipedia. Our experiments on two major triple-to-text datasets - WebNLG and E2E - show that our approach enables D2T generation from RDF triples in zero-shot settings.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: ACL 2022

  • ISBN

    978-1-955917-21-6

  • ISSN

  • e-ISSN

  • Number of pages

    19

  • Pages from-to

    3914-3932

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

    Stroudsburg, PA, USA

  • Event location

    Dublin, Ireland

  • Event date

    May 22, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article