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Found 7 papers, 4 papers with code
Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation
We perform multi-dimensional evaluation of model performance on mimicking both the style and linguistic differences that distinguish news of different credibility using machine translation metrics and classification models.
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.
Extracting Material Property Measurement Data from Scientific Articles
Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training data sets.
Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models
The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors.
Unsupervised Keyphrase Extraction via Interpretable Neural Networks
Keyphrase extraction aims at automatically extracting a list of "important" phrases representing the key concepts in a document.
Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations
Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications.
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.
