metadata

license: cc-by-nc-4.0
language:
  - en
metrics:
  - f1
pipeline_tag: text-classification
tags:
  - transformers
  - argument-mining
  - opinion-mining
  - information-extraction
  - inference-extraction
  - Twitter
widget:
  - text: 'Men shouldn’t be making laws about women’s bodies #abortion #Texas'
    example_title: Statement
  - text: >-
      ’Bitter truth’: EU chief pours cold water on idea of Brits keeping EU
      citizenship after #Brexit HTTPURL via @USER
    example_title: Notification
  - text: >-
      Opinion: As the draconian (and then some) abortion law takes effect in
      #Texas, this is not an idle question for millions of Americans. A slippery
      slope towards more like-minded Republican state legislatures to try to
      follow suit. #abortion #F24 HTTPURL
    example_title: Reason
  - text: '@USER Blah blah blah blah blah blah'
    example_title: None
  - text: republican men and karens make me sick
    example_title: Unlabeled 1
  - text: No empire lives forever! Historical fact! GodWins! 🙏💪🇺🇲
    example_title: Unlabeled 2
  - text: >-
      Further author information regarding registration and visa support letters
      will be sent to the authors soon. #CIKM2023
    example_title: Unlabeled 3
  - text: Ummmmmm
    example_title: Unlabeled 4
  - text: >-
      whoever says that The Last Jedi is a good movie is lying or trolling
      everyone
    example_title: Unlabeled 5
  - text: >-
      I don’t think people realize how big this story is GBI Strategies, the
      group paid $11M+ by Biden PACs to harvest fraudulent voter registrations
      in *20 states*, may be the root source of Democrat election rigging @USER
      may have just exposed their entire fraud machine HTTPURL
    example_tite: Unlabeled 6
base_model:
  - vinai/bertweet-base

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:

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)

Notice: The tweets need to undergo preprocessing before classification.

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:

Argument	No-Argument
865 (49.88%)	869 (50.12%)

Reason	Statement	Notification	None
581 (33.50%)	284 (16.38%)	500 (28.84%)	369 (21.28%)

Notice: Our training involved TACO to forecast class predictions, where the categories (Argument/No-Argument) represent class aggregations based on the inference component.

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

	Precision	Recall	F1-Score	Support
Reason	73.69%	75.22%	74.45%	581
Statement	54.37%	59.15%	56.66%	284
Notification	79.02%	77.60%	78.30%	500
None	83.87%	77.51%	80.56%	369
-------------	-----------	---------	----------	---------
Accuracy			73.76%	1734
Macro Avg	72.74%	72.37%	72.49%	1734
Weighted Avg	74.23%	73.76%	73.95%	1734

Categorization

	Precision	Recall	F1-Score	Support
No-Argument	86.66%	82.97%	84.77%	869
Argument	83.59%	87.17%	85.34%	865
-------------	-----------	---------	----------	---------
Accuracy			85.06%	1734
Macro Avg	85.13%	85.07%	85.06%	1734
Weighted Avg	85.13%	85.06%	85.06%	1734

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

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"
}