keimoriyama/TweetEval_Annotated

TweetEval-Annotated Dataset

TweetEval-Annotated is a crowdsourced annotation set built from the emoji recognition task in TweetEval. It contains the source label from TweetEval, the reference annotation label used in this dataset, four human annotations, and an optional LLM annotation.

Dataset Details

Dataset Sources [optional]

This dataset is created from the emoji recognition task in TweetEval dataset.

• Repository: cardiffnlp/tweet_eval

Uses

This dataset is useful for comparing human and LLM annotations against the TweetEval emoji task labels. It can be used for annotation analysis, agreement studies, and emoji classification experiments.

Dataset Structure

An instance of the dataset is:

  {
    "id": 574,
    "text": "Magical moonbeam light dance. Things are good.",
    "original_label": 5,
    "annotation_label": 3,
    "emoji_label": "😊",
    "annotation_0": "😊",
    "annotation_label_0": 3,
    "annotation_1": "😊",
    "annotation_label_1": 3,
    "annotation_2": "😊",
    "annotation_label_2": 3,
    "annotation_3": "😊",
    "annotation_label_3": 3,
    "annotatinon_llm": 3
  }

Data Fields

• id : id of the dataset
• text : target text
• original_label : original TweetEval label
• annotation_label : reference label for this annotated dataset
• emoji_label : emoji corresponding to the reference label
• annotation_[0-3] : emoji annotations from the four human annotators
• annotation_label_[0-3] : numeric labels for the four human annotations
• annotatinon_llm : numeric label predicted by the LLM, available only in the with_llm split

Splits

Split	Rows	Description
without_llm	3000	Human annotations only
with_llm	3000	Human annotations and LLM annotations. meta-llama/Llama-3.1-8B-Instruct is used for annotation.

Loading the dataset

from datasets import load_dataset

without_llm = load_dataset("keimoriyama/TweetEval_Annotated", split="without_llm")
with_llm = load_dataset("keimoriyama/TweetEval_Annotated", split="with_llm")

Citation

BibTeX:

@inproceedings{moriyama-etal-2026-task,
    title = "Task Assignment meets Annotator Modeling: Human-{LLM} Collaborative Annotation with Constraints",
    author = "Moriyama, Kei  and
      Nakayama, Kouta  and
      Baba, Yukino",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-srw.79/",
    pages = "888--902",
    ISBN = "979-8-89176-393-7",
    abstract = "Crowdsourced annotators and Large Language Models (LLMs) offer complementary, cost-effective ways to obtain labeled data, yet ensuring high label quality remains challenging.We observe that task features influence the accuracy of humans and LLMs, while real-world constraints, such as per-annotator assignment limits, further complicate allocation.Prior work typically addresses either task features or constraints, but not both.We present an integrated framework that (i) estimates per-task accuracy from task features using a \textit{learning from crowds} model and (ii) incorporates these estimations into a linear programming formulation that assigns tasks under practical constraints. Experimental results demonstrate that the proposed method achieves accuracy comparable to that of baseline methods while satisfying given constraints."
}