# Tribrid: Stance Classification with Neural Inconsistency Detection

arXiv: https://arxiv.org/abs/2109.06508 · HTML: https://mlsanity.com/paper/2109.06508/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://mlsanity.com/paper/1803.05449/) (2018)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples](https://mlsanity.com/paper/1803.01128/) (2018)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations](https://mlsanity.com/paper/1910.03065/) (2019)
- [The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants](https://mlsanity.com/paper/1708.01425/) (2017)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [A Challenge Set Approach to Evaluating Machine Translation](https://mlsanity.com/paper/1704.07431/) (2017)
- [Stance Detection Benchmark: How Robust Is Your Stance Detection?](https://mlsanity.com/paper/2001.01565/) (2020)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models](https://mlsanity.com/paper/1809.02079/) (2018)
- [Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM](https://mlsanity.com/paper/1704.07221/) (2017)
- [Automatic Stance Detection Using End-to-End Memory Networks](https://mlsanity.com/paper/1804.07581/) (2018)
- [Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News](https://mlsanity.com/paper/1811.00706/) (2018)
- [STANCY: Stance Classification Based on Consistency Cues](https://mlsanity.com/paper/1910.06048/) (2019)
- [Towards a Robust Deep Neural Network in Texts: A Survey](https://mlsanity.com/paper/1902.07285/) (2019)
- [Identifying Users with Opposing Opinions in Twitter Debates](https://mlsanity.com/paper/1402.7143/) (2014)
- [Estimating predictive uncertainty for rumour verification models](https://mlsanity.com/paper/2005.07174/) (2020)
- [Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue](https://mlsanity.com/paper/1709.00661/) (2017)
- [UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text](https://mlsanity.com/paper/1611.03599/) (2016)
- [Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos](https://mlsanity.com/paper/1703.09793/) (2017)

## Built on by (most cited first, 2 of 2)

- [Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study](https://mlsanity.com/paper/2404.03987/) (2024)
- [FarExStance: Explainable Stance Detection for Farsi](https://mlsanity.com/paper/2412.14008/) (2024)

## Abstract (arXiv)

We study the problem of performing automatic stance classification on social media with neural architectures such as BERT. Although these architectures deliver impressive results, their level is not yet comparable to the one of humans and they might produce errors that have a significant impact on the downstream task (e.g., fact-checking). To improve the performance, we present a new neural architecture where the input also includes automatically generated negated perspectives over a given claim. The model is jointly learned to make simultaneously multiple predictions, which can be used either to improve the classification of the original perspective or to filter out doubtful predictions. In the first case, we propose a weakly supervised method for combining the predictions into a final one. In the second case, we show that using the confidence scores to remove doubtful predictions allows our method to achieve human-like performance over the retained information, which is still a sizable part of the original input.