AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440551" target="_blank" >RIV/00216208:11320/21:10440551 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/21:00353374 RIV/68407700:21730/21:00353374
Result on the web
<a href="https://aclanthology.org/2021.nlp4convai-1.19/" target="_blank" >https://aclanthology.org/2021.nlp4convai-1.19/</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models
Original language description
Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for language model finetuning, and we employ massive data augmentation via back-translation to increase the diversity of the training data. We further examine the possibilities of combining data from multiples sources to improve performance on the target dataset. We carefully evaluate our contributions with both human and automatic methods. Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation.
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
<a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
3rd Worskhop on NLP for Conversational AI
ISBN
978-1-954085-86-2
ISSN
—
e-ISSN
—
Number of pages
13
Pages from-to
198-210
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburgh, PA, USA
Event location
Online
Event date
Nov 10, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—