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Found 11 papers, 2 papers with code
Improving Synonym Recommendation Using Sentence Context
Traditional synonym recommendations often include ill-suited suggestions for writer’s specific contexts.
Evaluating and Explaining Natural Language Generation with GenX
Current methods for evaluation of natural language generation models focus on measuring text quality but fail to probe the model creativity, i. e., its ability to generate novel but coherent text sequences not seen in the training corpus.
Evaluating Deception Detection Model Robustness To Linguistic Variation
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs.
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Our analyses reveal a significant drop in performance when testing neural models on out-of-domain data and non-English languages that may be mitigated using diverse training data.
Evaluating Neural Model Robustness for Machine Comprehension
We evaluate neural model robustness to adversarial attacks using different types of linguistic unit perturbations {--} character and word, and propose a new method for strategic sentence-level perturbations.
Hokey Pokey Causal Discovery: Using Deep Learning Model Errors to Learn Causal Structure
Many existing algorithms for the discovery of causal structure from observational data rely on evaluating the conditional independence relationships among features to account for the effects of confounding.
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
We evaluate machine comprehension models' robustness to noise and adversarial attacks by performing novel perturbations at the character, word, and sentence level.
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
We evaluate the predictive power of models trained on varied text and image representations extracted from tweets.
Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams
Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning.
