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Found 37 papers, 14 papers with code
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations
State-of-the-art machine learning models are prone to adversarial attacks”:" Maliciously crafted inputs to fool the model into making a wrong prediction, often with high confidence.
Understanding and Interpreting the Impact of User Context in Hate Speech Detection
As hate speech spreads on social media and online communities, research continues to work on its automatic detection.
Investigating Annotator Bias in Abusive Language Datasets
A prevalent form of bias in hate speech and abusive language datasets is annotator bias caused by the annotator’s subjective perception and the complexity of the annotation task.
Long Input Dialogue Summarization with Sketch Supervision for Summarization of Primetime Television Transcripts
This paper presents our entry to the CreativeSumm 2022 shared task.
Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics
To do so, we sample balanced subsets of data that are labeled by demographically distinct annotators.
Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers
Large-scale surveys are a widely used instrument to collect data from a target audience.
Investigating Annotator Bias with a Graph-Based Approach
In the scope of this study, we want to investigate annotator bias — a form of bias that annotators cause due to different knowledge in regards to the task and their subjective perception.
Impact of Politically Biased Data on Hate Speech Classification
One challenge that social media platforms are facing nowadays is hate speech.
TUM Social Computing at GermEval 2022: Towards the Significance of Text Statistics and Neural Embeddings in Text Complexity Prediction
In this paper, we describe our submission to the GermEval 2022 Shared Task on Text Complexity Assessment of German Text.
User Satisfaction Modeling with Domain Adaptation in Task-oriented Dialogue Systems
To the best of our knowledge, this is the first work to study user satisfaction estimation with unsupervised domain adaptation from chitchat to task-oriented dialogue.
GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer
Interpreting NLP models is fundamental for their development as it can shed light on hidden properties and unexpected behaviors.
SHAP-Based Explanation Methods: A Review for NLP Interpretability
Model explanations are crucial for the transparent, safe, and trustworthy deployment of machine learning models.
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks
Adversarial attacks are a major challenge faced by current machine learning research.
Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?
Text simplification seeks to improve readability while retaining the original content and meaning.
Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches
Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident.
This is not correct! Negation-aware Evaluation of Language Generation Systems
Based on this dataset, we fine-tuned a sentence transformer and an evaluation metric to improve their negation sensitivity.
Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training
Moreover, with the style-specific pre-training, we reduced the number of trainable parameters in text simplification models.
AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks.
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
This paper presents the best-performing approach alias "Adam Smith" for the SemEval-2023 Task 4: "Identification of Human Values behind Arguments".
IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models
Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications.
From Judgement's Premises Towards Key Points
Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments.
Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis
Then, the pipeline uses image retrieval to find all images showing similar content and applies aspect-based sentiment analysis to outline users' opinions about the selected term.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks
Adversarial attacks are a major challenge faced by current machine learning research.
How to Build Robust FAQ Chatbot with Controllable Question Generator?
Many unanswerable adversarial questions fool the question-answer (QA) system with some plausible answers.
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis
Sentiment analysis is often a crowdsourcing task prone to subjective labels given by many annotators.
A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining
User-generated content from social media is produced in many languages, making it technically challenging to compare the discussed themes from one domain across different cultures and regions.
An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder
Commonly, introductory programming courses in higher education institutions have hundreds of participating students eager to learn to program.
SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining
In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit.
Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community
Since traditional social media platforms continue to ban actors spreading hate speech or other forms of abusive languages (a process known as deplatforming), these actors migrate to alternative platforms that do not moderate users content.
Classification of Consumer Belief Statements From Social Media
For scenario b) we compare abstract class labels given by the domain expert as baseline and by automatic hierarchical clustering.
GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts
To unfold the tremendous amount of multimedia data uploaded daily to social media platforms, effective topic modeling techniques are needed.
Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings
With the explosive growth in textual data, it is becoming increasingly important to summarize text automatically.
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing
A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning.
An evaluation of keyword extraction from online communication for the characterisation of social relations
The set of interpersonal relationships on a social network service or a similar online community is usually highly heterogenous.
