Transfer Learning and Masked Generation for Answer Verbalization

Kód výsledku v IS VaVaI

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

Výsledek na webu

<a href="https://aclanthology.org/2022.suki-1.6" target="_blank" >https://aclanthology.org/2022.suki-1.6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2022.suki-1.6" target="_blank" >10.18653/v1/2022.suki-1.6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Transfer Learning and Masked Generation for Answer Verbalization

Popis výsledku v původním jazyce

Structured Knowledge has recently emerged as an essential component to support fine-grained Question Answering (QA). In general, QA systems query a Knowledge Base (KB) to detect and extract the raw answers as final prediction. However, as lacking of context, language generation can offer a much informative and complete response. In this paper, we propose to combine the power of transfer learning and the advantage of entity placeholders to produce high-quality verbalization of extracted answers from a KB. We claim that such approach is especially well-suited for answer generation. Our experiments show 44.25%, 3.26% and 29.10% relative gain in BLEU over the state-of-the-art on the VQuAnDA, ParaQA and VANiLLa datasets, respectively. We additionally provide minor hallucinations corrections in VANiLLa standing for 5% of each of the training and testing set. We witness a median absolute gain of 0.81 SacreBLEU. This strengthens the importance of data quality when using automated evaluation.

Název v anglickém jazyce

Transfer Learning and Masked Generation for Answer Verbalization

Popis výsledku anglicky

Structured Knowledge has recently emerged as an essential component to support fine-grained Question Answering (QA). In general, QA systems query a Knowledge Base (KB) to detect and extract the raw answers as final prediction. However, as lacking of context, language generation can offer a much informative and complete response. In this paper, we propose to combine the power of transfer learning and the advantage of entity placeholders to produce high-quality verbalization of extracted answers from a KB. We claim that such approach is especially well-suited for answer generation. Our experiments show 44.25%, 3.26% and 29.10% relative gain in BLEU over the state-of-the-art on the VQuAnDA, ParaQA and VANiLLa datasets, respectively. We additionally provide minor hallucinations corrections in VANiLLa standing for 5% of each of the training and testing set. We witness a median absolute gain of 0.81 SacreBLEU. This strengthens the importance of data quality when using automated evaluation.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

ISBN

978-1-955917-86-5

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

47-54

Název nakladatele

Association for Computational Linguistics

Místo vydání

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Místo konání akce

Seattle, USA

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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