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Found 6 papers, 3 papers with code
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.
Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP
Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as freetext.
Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple Metric
Our proposed metric achieves state-of-the-art performance on the HUMOD dataset while reducing measured sensitivity to dataset by 37%-66%.
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata
An ultimate goal of the AI and ML fields is artificial general intelligence (AGI); although such systems remain science fiction, various models exhibit aspects of AGI.
On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems
Automatically evaluating text-based, non-task-oriented dialogue systems (i. e., `chatbots') remains an open problem.
Attribute Diversity Determines the Systematicity Gap in VQA
The degree to which neural networks can generalize to new combinations of familiar concepts, and the conditions under which they are able to do so, has long been an open question.
