Search Results for author: George Chrysostomou

Found 7 papers, 6 papers with code

On the Impact of Temporal Concept Drift on Model Explanations

1 code implementation17 Oct 2022 Zhixue Zhao, George Chrysostomou, Kalina Bontcheva, Nikolaos Aletras

Explanation faithfulness of model predictions in natural language processing is typically evaluated on held-out data from the same temporal distribution as the training data (i. e. synchronous settings).

Text Classification

An Empirical Study on Explanations in Out-of-Domain Settings

1 code implementation ACL 2022 George Chrysostomou, Nikolaos Aletras

Recent work in Natural Language Processing has focused on developing approaches that extract faithful explanations, either via identifying the most important tokens in the input (i. e. post-hoc explanations) or by designing inherently faithful models that first select the most important tokens and then use them to predict the correct label (i. e. select-then-predict models).

Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

1 code implementation EMNLP 2021 Atsuki Yamaguchi, George Chrysostomou, Katerina Margatina, Nikolaos Aletras

Masked language modeling (MLM), a self-supervised pretraining objective, is widely used in natural language processing for learning text representations.

Language Modelling Masked Language Modeling

Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience

1 code implementation EMNLP 2021 George Chrysostomou, Nikolaos Aletras

In this paper, we hypothesize that salient information extracted a priori from the training data can complement the task-specific information learned by the model during fine-tuning on a downstream task.

Flexible Instance-Specific Rationalization of NLP Models

1 code implementation16 Apr 2021 George Chrysostomou, Nikolaos Aletras

Recent research on model interpretability in natural language processing extensively uses feature scoring methods for identifying which parts of the input are the most important for a model to make a prediction (i. e. explanation or rationale).

General Classification text-classification +1

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