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IJCNLP-AACL 2023 : The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics

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Link: http://www.ijcnlp-aacl2023.org/
 
When Nov 1, 2023 - Nov 4, 2023
Where Bali, Indonesia
Submission Deadline May 23, 2023
Notification Due Sep 4, 2023
Final Version Due Sep 20, 2023
Categories    NLP   ML
 

Call For Papers

============================

IJCNLP-AACL 2023
The 13th International Joint Conference on Natural Language Processing and
the 3rd Conference of the Asia-Pacific Chapter of the Association for
Computational Linguistics

Website: http://www.ijcnlp-aacl2023.org/
Submission Deadline:
- START direct submission: 23 May 2023
- ARR commitment due: 15 August 2023

Main Conference Dates: November 1-4 2023
Location: Bali, Indonesia
Theme: Large Language Models (LLMs) and Regional/Low-Resource Languages
Contact:
- Jong Park (General Chair)
- Yuki Arase, Baotian Hu, Wei Lu (Program Chairs):
ijcnlp-aacl-2023-pc [at] googlegroups.com

============================

Call for Main Conference Papers

IJCNLP-AACL 2023 invites the submission of long and short papers featuring
substantial, original, and unpublished research in all aspects of
Computational Linguistics and Natural Language Processing.

=== Important Dates ===

Anonymity period for papers submitted through START: April 23, 2023
Direct paper submission deadline: May 23, 2023
Commitment deadline for ARR papers: August 15, 2023
Author response period: August 2-9, 2023
Notification of acceptance: September 4, 2023
Conference: November 1-4, 2023

All deadlines are 11:59 PM UTC-12:00 (“anywhere on Earth”).

=== Submission Topics ===

IJCNLP-AACL 2023 aims to have a broad technical program. Relevant topics for the
conference include, but are not limited to, the following areas (in alphabetical order):

- Computational Social Science and Cultural Analytics
- Dialogue and Interactive Systems
- Discourse and Pragmatics
- Ethics and NLP
- Generation
- Information Extraction
- Information Retrieval and Text Mining
- Interpretability and Analysis of Models for NLP
- Language Grounding to Vision, Robotics and Beyond
- Multilingualism and Language Contact: Code-switching, Representation Learning, Cross-lingual transfer
- Linguistic Theories, Cognitive Modeling, and Psycholinguistics
- Machine Learning for NLP
- Machine Translation
- NLP Applications
- Phonology, Morphology, and Word Segmentation
- Question Answering
- Resources and Evaluation
- Semantics: Lexical
- Semantics: Sentence-level Semantics, Textual Inference, and Other Areas
- Sentiment Analysis, Stylistic Analysis, and Argument Mining
- Speech and Multimodality
- Summarization
- Syntax: Tagging, Chunking and Parsing
- Theme Track: Large Language Models (LLMs) and Regional/Low-Resource Languages

=== Theme Track: Large Language Models (LLMs) and Regional/Low-Resource Languages ===

LLMs (large language models) have been a major breakthrough in NLP, allowing machines to process
and understand human language with unprecedented effectiveness and efficiency.
However, LLMs are predominantly utilized for widely-spoken languages like English, Chinese, and Spanish.
However, the development (e.g., pre-training or fine-tuning) and utilization of LLMs for regional languages,
such as those spoken in ASEAN countries, as well as low-resource languages, often receive insufficient attention.

The inadequate development and utilization of LLMs for regional and low-resource languages is a significant issue.
Many people around the world speak such languages as their primary language, which often have unique grammatical
structures, vocabulary, and cultural elements that are not easily translatable to other languages.
The neglect of these languages in LLM research and development may hinder the creation of effective NLP tools for such
languages, resulting in the linguistic and cultural exclusion of those who use them.

Improving the situation for regional and low-resource languages requires researchers and developers
to prioritize the development of LLMs specifically designed for these languages, through pre-training or fine-tuning,
and with sufficient consideration given to the context of how these languages are typically used by their speakers
(e.g., code-mixed with local dialects or English).
This may involve developing new models that account for the unique features of each language or adapting existing models
to work with languages that have limited data available. Another crucial research topic is exploring how existing LLMs
can better support the processing of such languages, including in the downstream applications.
Furthermore, efforts should be made to collect and curate high-quality language data to train and evaluate
LLMs for these languages, both in the aspects of capability and value alignment.

In IJCNLP-AACL 2023, we are delighted to announce a special theme on "Large Language Models (LLMs) and Regional/Low-Resource Languages".
We welcome submissions from researchers on a range of topics within this theme, including position papers, opinion pieces,
modeling studies, resource papers, and application papers. Possible topics of interest include (but are not limited to):

- Developing effective or efficient pre-training and fine-tuning techniques for large language models
in regional/low-resource languages.
- Evaluating the effectiveness of current large language models on regional and low-resource languages,
and identifying areas for improvement.
- Investigating the impact of pre-training and fine-tuning large language models on linguistic and
cultural diversity for regional/low-resource languages.
- Developing strategies for creating high-quality data for pre-training or fine-tuning in regional/low-resource languages
to improve the performance of large language models.
- Assessing the ethical considerations of using large language models in regional/low-resource languages,
including issues of linguistic and cultural bias.
- Proposing techniques to manage regional-specific or emerging linguistic issues such as code-mixing,
informal forms of regional languages in social media and English as used by regional (e.g., Southeast Asian) speakers.
- Exploring the potential of transfer learning or adaptation strategies to improve the performance of large language models
in regional/low-resource languages, including those that take advantage of the common linguistic roots of some related
regional languages or dialects.

=== Formatting Guidelines ===

Long papers must describe substantial, original, completed, and unpublished work. Long papers may consist of up to eight (8) pages of
content, plus unlimited pages of references and appendices.
Short papers should have a small, focused contribution. Short papers may consist of up to four (4) pages of content, plus unlimited
pages of references and appendices. Final versions of both long and short papers will be given one additional page of content
so that reviewers' comments can be taken into account.

Both long and short papers must follow the ACL 2023 two-column format [1], using the official style files.

IJCNLP-AACL 2023 also requires a "Limitations" section to clearly discuss the limitations of work and encourages
including an explicit ethics statement (both sections will not count towards the page limit).

=== Multiple Submission Policy ===

IJCNLP-AACL 2023 precludes multiple submissions. IJCNLP-AACL 2023 will not consider any paper that is under review in a journal
or another conference at the time of submission, and submitted papers must not be submitted elsewhere during the review period.
In addition, we will not consider any paper that overlaps significantly in content or results with papers that will be (or have been)
published elsewhere, without exception.


[1] https://2023.aclweb.org/calls/style_and_formatting/

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