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EMNLP 2024 : The 2024 Conference on Empirical Methods in Natural Language Processing

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Conference Series : Empirical Methods in Natural Language Processing
 
Link: https://2024.emnlp.org/
 
When Nov 12, 2024 - Nov 16, 2024
Where Miami, Florida
Submission Deadline Jun 15, 2024
Notification Due Sep 20, 2024
Final Version Due Oct 3, 2024
Categories    natural language processing   NLP
 

Call For Papers

EMNLP 2024 invites the submission of long and short papers featuring substantial, original, and unpublished research on empirical methods for Natural Language Processing. EMNLP2024 has a goal of a diverse technical program—in addition to traditional research results, papers may contribute negative findings, survey an area, announce the creation of a new resource, argue a position, report novel linguistic insights derived using existing computational techniques, and reproduce, or fail to reproduce, previous results. As in recent years, some of the presentations at the conference will be of papers accepted by the Transactions of the ACL (TACL) and the Computational Linguistics (CL) journals.

EMNLP 2024 aims to have a broad technical program. Relevant topics for the conference include, but are not limited to, the following areas:

- Computational Social Science and Cultural Analytics
- Dialogue and Interactive Systems
- Discourse and Pragmatics
- Low-resource Methods for NLP
- Ethics, Bias, and Fairness
- Generation
- Information Extraction
- Information Retrieval and Text Mining
- Interpretability and Analysis of Models for NLP
- Linguistic theories, Cognitive Modeling and Psycholinguistics
- Machine Learning for NLP
- Machine Translation
- Multilinguality and Language Diversity
- Multimodality and Language Grounding to Vision, Robotics and Beyond
- Phonology, Morphology and Word Segmentation
- Question Answering
- Resources and Evaluation
- Semantics: Lexical, Sentence-level Semantics, Textual Inference and Other areas
- Sentiment Analysis, Stylistic Analysis, and Argument Mining
- Speech processing and spoken language understanding
- Summarization
- Syntax: Tagging, Chunking and Parsing
- NLP Applications
- Special Theme: Efficiency in Model Algorithms, Training, and Inference

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