WRAPresentations EU AI Act Compliance Profile

WRAPresentations -- A TACO-based Embedder For Inference and Information-Driven Argument Mining on Twitter

Introducing WRAPresentations, a cutting-edge sentence-transformers model that leverages the power of a 768-dimensional dense vector space to map tweets according to the four classes Reason, Statement, Notification and None. This powerful model is tailored for argument mining on Twitter, derived from the BERTweet-base architecture initially pre-trained on Twitter data. Through fine-tuning with the TACO dataset, WRAPresentations is effectively in encoding inference and information in tweets.

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..

WRAPresentations, to some degree, captures the semantics of these critical components in its embedding space.

Consequently, it has also learned the class semantics, where inferences and information can be aggregated in relation to these distinct classes containing these components:

• 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 a

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