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会议全称：Conference on Empirical Methods in Natural Language Processing
录用率：2021年23.33%
CCF分级：人工智能B
截稿时间：2024/6/15
录用通知时间：2024/9/20
官网链接：
https://2024.emnlp.org/
征稿范围：
EMNLP
2024 aims to have a broad technical program. Relevant topics for the
conference include, but are not limited to, the following areas:
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Computational Social Science and Cultural Analytics
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Dialogue and Interactive Systems
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Discourse and Pragmatics
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Low-resource Methods for NLP
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Ethics, Bias, and Fairness
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Generation
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Information Extraction
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Information Retrieval and Text Mining
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Interpretability and Analysis of Models for NLP
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Linguistic theories, Cognitive Modeling and Psycholinguistics
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Machine Learning for NLP
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Machine Translation
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Multilinguality and Language Diversity
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Multimodality and Language Grounding to Vision, Robotics and Beyond
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Phonology, Morphology and Word Segmentation
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Question Answering
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Resources and Evaluation
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Semantics: Lexical, Sentence-level Semantics, Textual Inference and Other areas
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Sentiment Analysis, Stylistic Analysis, and Argument Mining
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Speech processing and spoken language understanding
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Summarization
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Syntax: Tagging, Chunking and Parsing
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NLP Applications
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Special Theme:
Efficiency in Model Algorithms, Training, and Inference
EMNLP 2024 Theme Track: Efficiency in Model Algorithms, Training, and Inference
This
track provides a platform for researchers to explore key aspects of
making model algorithms, training, and inference more efficient, e.g.,
quantization, data requirements, and model size. We welcome
submissions that propose innovative approaches, methodologies, and
techniques to streamline the training and inference process for language
models while optimizing resource utilization and reducing model size.
Authors are encouraged to explore various ways to enhance efficiency,
including parameter-efficient tuning and methods for learning with less
data and smaller model sizes, ultimately leading to more scalable,
practical, and resource-efficient NLP systems.
