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Found 4 papers, 1 papers with code
Explaining Recommendation System Using Counterfactual Textual Explanations
One method for producing a more explainable output is using counterfactual reasoning, which involves altering minimal features to generate a counterfactual item that results in changing the output of the system.
Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME
On the other hand, in some tasks such as medical, economic, and self-driving cars, users want the model to be interpretable to decide if they can trust these results or not.
Lotus at SemEval-2021 Task 2: Combination of BERT and Paraphrasing for English Word Sense Disambiguation
In this paper, we describe our proposed methods for the multilingual word-in-Context disambiguation task in SemEval-2021.
Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity
We use FarsNet, the Persian Word Net, besides deep learning techniques to extract the similarity of words.
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