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Found 18 papers, 7 papers with code
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation
Negation is underrepresented in existing natural language inference benchmarks.
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
The State of Human-centered NLP Technology for Fact-checking
We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption.
InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples
Specifically, we focus on measuring and improving the performance of machine learning systems on negation-based adversarial examples and their ability to generalize across out-of-distribution topics.
Paraphrasing, textual entailment, and semantic similarity above word level
This dissertation explores the linguistic and computational aspects of the meaning relations that can hold between two or more complex linguistic expressions (phrases, clauses, sentences, paragraphs).
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection
In this work, we first introduce a differentiable measure that enables direct optimization of group fairness (specifically, balancing accuracy across groups) in model training.
ProtoTEx: Explaining Model Decisions with Prototype Tensors
We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability
To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children's performance on tests of mindreading, consisting of 10, 320 question-answer pairs.
``What is on your mind?'' Automated Scoring of Mindreading in Childhood and Early Adolescence
In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence.
"What is on your mind?" Automated Scoring of Mindreading in Childhood and Early Adolescence
In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence.
Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity
We use the typology to annotate a corpus of 520 sentence pairs in English and we demonstrate that unlike previous typologies, SHARel can be applied to all relations of interest with a high inter-annotator agreement.
A Qualitative Evaluation Framework for Paraphrase Identification
In this paper, we present a new approach for the evaluation, error analysis, and interpretation of supervised and unsupervised Paraphrase Identification (PI) systems.
Annotating and analyzing the interactions between meaning relations
Here we present a corpus annotated with these relations and the analysis of these results.
WARP-Text: a Web-Based Tool for Annotating Relationships between Pairs of Texts
We present WARP-Text, an open-source web-based tool for annotating relationships between pairs of texts.
