Overview

EMNLP 2024 invites the submission of long and short papers featuring substantial, original, and unpublished research on empirical methods for Natural Language Processing.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.

Paper Submission Information

Papers may be submitted to the ARR 2024 June cycle (link to be provided later). Papers that have received reviews and a meta-review from ARR (whether from the ARR 2024 June cycle or an earlier ARR cycle) may be committed to EMNLP (link to be provided later).

Mandatory Reviewing Workload (NEW!!)

As our pace of research continues to increase, we need to strengthen the commitment to reviewing for each paper submission. During the ARR submission process, authors will be required to specify which co-authors are committing to cover reviewing in this reviewing cycle. Please see the new ARR policy regarding reviewing workload here. As this is an ARR-wide policy for all *CL conferences, questions or clarifications should be addressed to ARR directly.

Submission Topics

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