Transfer Learning and Masked Generation for Answer Verbalization
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Transfer Learning and Masked Generation for Answer Verbalization
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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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 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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Number of pages
8
Pages from-to
47-54
Publisher name
Association for Computational Linguistics
Place of publication
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Event location
Seattle, USA
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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