Search Results for author:
Found 16 papers, 8 papers with code
HILDIF: Interactive Debugging of NLI Models Using Influence Functions
Biases and artifacts in training data can cause unwelcome behavior in text classifiers (such as shallow pattern matching), leading to lack of generalizability.
Label-Aware Automatic Verbalizer for Few-Shot Text Classification
Specifically, we use the manual labels along with the conjunction "and" to induce the model to generate more effective words for the verbalizer.
Towards Explainable Evaluation Metrics for Machine Translation
In this context, we also discuss the latest state-of-the-art approaches to explainable metrics based on generative models such as ChatGPT and GPT4.
Argumentative Explanations for Pattern-Based Text Classifiers
Hence, we propose AXPLR, a novel explanation method using (forms of) computational argumentation to generate explanations (for outputs computed by PLR) which unearth model agreements and disagreements among the features.
Towards Explainable Evaluation Metrics for Natural Language Generation
We also provide a synthesizing overview over recent approaches for explainable machine translation metrics and discuss how they relate to those goals and properties.
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results
In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation.
Rational LAMOL: A Rationale-based Lifelong Learning Framework
Lifelong learning (LL) aims to train a neural network on a stream of tasks while retaining knowledge from previous tasks.
Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies
Health professional regulators aim to protect the health and well-being of patients and the public by setting standards for scrutinising and overseeing the training and conduct of health and care professionals.
ESRA: Explainable Scientific Research Assistant
We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems.
Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems
Practically, some combinations of slot values can be invalid according to external knowledge.
Explanation-Based Human Debugging of NLP Models: A Survey
Debugging a machine learning model is hard since the bug usually involves the training data and the learning process.
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Data exploration is an important step of every data science and machine learning project, including those involving textual data.
Deep Argumentative Explanations
Despite the recent, widespread focus on eXplainable AI (XAI), explanations computed by XAI methods tend to provide little insight into the functioning of Neural Networks (NNs).
Explainable Artificial Intelligence (XAI)
Text Classification
FIND: Human-in-the-Loop Debugging Deep Text Classifiers
Since obtaining a perfect training dataset (i. e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets.
Human-grounded Evaluations of Explanation Methods for Text Classification
Due to the black-box nature of deep learning models, methods for explaining the models' results are crucial to gain trust from humans and support collaboration between AIs and humans.
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification.
