Search Results for author:
Found 11 papers, 5 papers with code
Dissecting Paraphrases: The Impact of Prompt Syntax and supplementary Information on Knowledge Retrieval from Pretrained Language Models
Typically, designing these prompts is a tedious task because small differences in syntax or semantics can have a substantial impact on knowledge retrieval performance.
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles
Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
Detecting Harmful Memes and Their Targets
In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
We focus on two tasks: (i)detecting harmful memes, and (ii)identifying the social entities they target.
Detecting Propaganda Techniques in Memes
We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.
TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods.
A Mixed Initiative Semantic Web Framework for Process Composition
Semantic Web technologies offer the prospect of significantly reducing the amount of effort required to integrate existing enterprise functionality in support of new composite processes; whether within a given organization or across multiple ones.
Training Neural Machines with Trace-Based Supervision
We investigate the effectiveness of trace-based supervision methods for training existing neural abstract machines.
Training Neural Machines with Partial Traces
We present a novel approach for training neural abstract architectures which in- corporates (partial) supervision over the machine’s interpretable components.
