Argument Mining with Modular BERT and Transfer Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00577167" target="_blank" >RIV/67985807:_____/23:00577167 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/IJCNN54540.2023.10191968" target="_blank" >http://dx.doi.org/10.1109/IJCNN54540.2023.10191968</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN54540.2023.10191968" target="_blank" >10.1109/IJCNN54540.2023.10191968</a>

Result language

angličtina

Original language name

Argument Mining with Modular BERT and Transfer Learning

Original language description

We introduce BERT-MINUS, a modular, feature-enriched and transfer learning enabled model for Argument Mining. BERT-MINUS consists of: 1) a joint module which embeds the paragraph text, and 2) a dedicated module, consisting of three customized BERT models, which contextualize the argument markers, argument components and additional features given as text, respectively. BERT-MINUS implements two kinds of transfer learning - auto-transfer (transfer from a task to itself) and cross-transfer (classical transfer from one task to another) - via a novel Selective Fine-tuning mechanism. BERT-MINUS achieves state-of-the-art results on the Link Identification task and competitive results on the Argument Type Classification task. The synergy between the Features as Text and Selective Fine-tuning mechanisms significantly improves the performance of the model. Our work reveals the importance and potential of transfer learning via selective fine-tuning for modular Language Models. Moreover, this study dovetails naturally into the Prompt Engineering paradigm in NLP.

Czech name

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Czech description

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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)

Project

<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

IJCNN 2023 Conference Proceedings

ISBN

978-1-6654-8867-9

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

191330

Publisher name

IEEE

Place of publication

Piscataway

Event location

Queensland

Event date

Jun 18, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001046198707037