loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Enes Burak Dündar ; Osman Fatih Kılıç ; Tolga Çekiç ; Yusufcan Manav and Onur Deniz

Affiliation: Natural Language Processing Department, Yapi Kredi Technology, Istanbul, Turkey

Keyword(s): Intent Detection, Text Classification, Chatbot, Language Modeling.

Abstract: We have developed a large-scale intent detection method for our Turkish conversation system in banking domain to understand the problems of our customers. Recent advancements in natural language processing(NLP) have allowed machines to understand the words in a context by using their low dimensional vector representations a.k.a. contextual word embeddings. Thus, we have decided to use two language model architectures that provide contextual embeddings: ELMo and BERT. We trained ELMo on Turkish corpora while we used a pretrained Turkish BERT model. To evaluate these models on an intent classification task, we have collected and annotated 6453 customer messages in 148 intents. Furthermore, another Turkish document classification dataset named Kemik News are used for comparing our method with the state-of-the-art models. Experimental results have shown that using contextual word embeddings boost Turkish document classification performance on various tasks. Moreover, converting Turkish c haracters to English counterparts results in a slightly better performance. Lastly, an experiment is conducted to find out which BERT layer is more effective to use for intent classification task. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.118.2.15

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Dündar, E.; Kılıç, O.; Çekiç, T.; Manav, Y. and Deniz, O. (2020). Large Scale Intent Detection in Turkish Short Sentences with Contextual Word Embeddings. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR; ISBN 978-989-758-474-9; ISSN 2184-3228, SciTePress, pages 187-192. DOI: 10.5220/0010108301870192

@conference{kdir20,
author={Enes Burak Dündar. and Osman Fatih Kılı\c{C}. and Tolga \c{C}eki\c{C}. and Yusufcan Manav. and Onur Deniz.},
title={Large Scale Intent Detection in Turkish Short Sentences with Contextual Word Embeddings},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR},
year={2020},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010108301870192},
isbn={978-989-758-474-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR
TI - Large Scale Intent Detection in Turkish Short Sentences with Contextual Word Embeddings
SN - 978-989-758-474-9
IS - 2184-3228
AU - Dündar, E.
AU - Kılıç, O.
AU - Çekiç, T.
AU - Manav, Y.
AU - Deniz, O.
PY - 2020
SP - 187
EP - 192
DO - 10.5220/0010108301870192
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic