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Found 19 papers, 4 papers with code
Textual Summarisation of Large Sets: Towards a General Approach
We are developing techniques to generate summary descriptions of sets of objects.
Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration
Then we utilize the class-wise miscalibration score as a proxy to design a calibration technique that can tackle both over and under-confidence.
Empirical Optimal Risk to Quantify Model Trustworthiness for Failure Detection
We propose the Excess Area Under the Optimal RC Curve (E-AUoptRC), with the area in coverage from the optimal point to the full coverage.
Confidence-Aware Calibration and Scoring Functions for Curriculum Learning
In this paper we integrate notions of model confidence and human confidence with label smoothing, respectively \textit{Model Confidence LS} and \textit{Human Confidence LS}, to achieve better model calibration and generalization.
Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics
Our experimental results on three real-world datasets show that both the extractive and abstractive approaches outperform four strong baselines in terms of facilitating topic understanding and interpretation.
Summarising Historical Text in Modern Languages
We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language.
WordNet-feelings: A linguistic categorisation of human feelings
We then proceeded to annotate 11386 WordNet senses of all these words to create WordNet-feelings, a new affective dataset that identifies 3664 word senses as feelings, and associates each of these with one of the 9 categories of feeling.
Generating Summaries of Sets of Consumer Products: Learning from Experiments
We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset.
