TACO

TACO -- Twitter Arguments from COnversations

Introducing TACO, a baseline classification model built upon AutoModelForSequenceClassification, designed to identify tweets belonging to four distinct classes: Reason, Statement, Notification, and None. Tailored specifically for argument mining on Twitter, this baseline model is an evolution of the BERTweet-base architecture, which was originally pre-trained on Twitter data. Through fine-tuning with the TACO dataset, the baseline model acquires its name and excels in the extraction of Twitter Arguments from COnversations.

Class Semantics

The TACO framework revolves around the two key elements of an argument, as defined by the Cambridge Dictionary. It encodes inference as a guess that you make or an opinion that you form based on the information that you have, and it also leverages the definition of information as facts or details about a person, company, product, etc..

Taken together, the following classes of tweets can be identified by TACO:

• Statement, which refers to unique cases where only the inference is presented as something that someone says or writes officially, or an action done to express an opinion.
• Reason, which represents a full argument where the inference is based on direct information mentioned in the tweet, such as a source-reference or quotation, and thus reveals the author’s motivation to try to understand and to make judgments based on practical facts.
• Notification, which refers to a tweet that limits itself to providing information, such as media channels promoting their latest articles.
• None, a tweet that provides neither inference nor information.

In its entirety, TACO can classify the following hierarchy for tweets:

<div align="center"> <img src="https://github.com/TomatenMarc/public-images/raw/main/Argument_Tree.svg" alt="Argument Tree" width="100%"> </div>

Usage

Using this model becomes easy when you have transformers installed:

pip install - U transformers

Then you can use the model to generate tweet classifications like this:

from transformers import pipeline

pipe = pipeline("text-classification", model="TomatenMarc/TACO")
prediction = pipe("Huggingface is awesome")

print(prediction)

<a href="https://github.com/TomatenMarc/TACO/blob/main/notebooks/classifier_cv.ipynb"> <blockquote style="border-left: 5px solid grey; background-color: #f0f5ff; padding: 10px;"> Notice: The tweets need to undergo preprocessing before classification. </blockquote> </a>

Training

The final model underwent training using the entire shuffled ground truth dataset known as TACO, encompassing a total of 1734 tweets. This dataset showcases the distribution of topics as: #abortion (25.9%), #brexit (29.0%), #got (11.0%), #lotrrop (12.1%), #squidgame (12.7%), and #twittertakeover (9.3%). For training, we utilized SimpleTransformers.

Additionally, the category and class distribution of the dataset TACO is as follows:

<p> <blockquote style="border-left: 5px solid grey; background-color: #f0f5ff; padding: 10px;"> Notice: Our training involved TACO to forecast class predictions, where the categories (Argument/No-Argument) represent class aggregations based on the inference component. </blockquote> <p>

Dataloader

"data_loader": {
    "type": "torch.utils.data.dataloader.DataLoader",
    "args": {
        "batch_size": 8,
        "sampler": "torch.utils.data.sampler.RandomSampler"
    }
}

Parameters of the fit()-Method:

{
    "epochs": 5,
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 4e-05
    },
    "scheduler": "WarmupLinear",
    "warmup_steps": 66,
    "weight_decay": 0.06
}

Evaluation

We utilized a stratified 10-fold cross-validation approach to present TACO's performance. In doing so, we employed the identical data and parameters as outlined in the Training section. This involved training on k-1 splits and utilizing the kth split for making predictions.

In total, the TACO classifier performs as follows:

Classification

Categorization

Environmental Impact

• Hardware Type: A100 PCIe 40GB
• Hours used: 10 min
• Cloud Provider: Google Cloud Platform
• Compute Region: asia-southeast1 (Singapore)
• Carbon Emitted: 0.02kg CO2

Licensing

TACO © 2023 is licensed under CC BY-NC-SA 4.0

Citation

@inproceedings{feger-dietze-2024-taco,
    title = "{TACO} {--} {T}witter Arguments from {CO}nversations",
    author = "Feger, Marc  and
              Dietze, Stefan",
    editor = "Calzolari, Nicoletta  and
              Kan, Min-Yen  and
              Hoste, Veronique  and
              Lenci, Alessandro  and
              Sakti, Sakriani  and
              Xue, Nianwen",
              booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1349",
    pages = "15522--15529"
}