# DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation

arXiv: https://arxiv.org/abs/2108.13140 · HTML: https://mlsanity.com/paper/2108.13140/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 33 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 26 of 26)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Rationalizing Neural Predictions](https://mlsanity.com/paper/1606.04155/) (2016)
- [Analysis Methods in Neural Language Processing: A Survey](https://mlsanity.com/paper/1812.08951/) (2018)
- [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://mlsanity.com/paper/1805.02266/) (2018)
- [Learning to Faithfully Rationalize by Construction](https://mlsanity.com/paper/2005.00115/) (2020)
- [Learning to Deceive with Attention-Based Explanations](https://mlsanity.com/paper/1909.07913/) (2019)
- [SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis](https://mlsanity.com/paper/2005.05635/) (2020)
- [The Promise and Peril of Human Evaluation for Model Interpretability](https://mlsanity.com/paper/1711.07414/) (2017)
- [Evaluating Saliency Methods for Neural Language Models](https://mlsanity.com/paper/2104.05824/) (2021)
- [Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task](https://mlsanity.com/paper/1711.01505/) (2017)

## Built on by (most cited first, 5 of 5)

- [Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives](https://mlsanity.com/paper/2406.05615/) (2024)
- [Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation](https://mlsanity.com/paper/2205.12593/) (2022)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [An Interpretability Evaluation Benchmark for Pre-trained Language Models](https://mlsanity.com/paper/2207.13948/) (2022)
- [Towards Transparent AI: A Survey on Explainable Language Models](https://mlsanity.com/paper/2509.21631/) (2025)

## Abstract (arXiv)

While deep learning models have greatly improved the performance of most artificial intelligence tasks, they are often criticized to be untrustworthy due to the black-box problem. Consequently, many works have been proposed to study the trustworthiness of deep learning. However, as most open datasets are designed for evaluating the accuracy of model outputs, there is still a lack of appropriate datasets for evaluating the inner workings of neural networks. The lack of datasets obviously hinders the development of trustworthiness research. Therefore, in order to systematically evaluate the factors for building trustworthy systems, we propose a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability. To evaluate these factors, our dataset contains diverse annotations about the challenging distribution of instances, manual adversarial instances and sentiment explanations. Several evaluation metrics are further proposed for interpretability and robustness. Based on the dataset and metrics, we conduct comprehensive comparisons for the trustworthiness of three typical models, and also study the relations between accuracy, robustness and interpretability. We release this trustworthiness evaluation dataset at urlhttps://github/xyz and hope our work can facilitate the progress on building more trustworthy systems for real-world applications.