# PU-Lie: Lightweight Deception Detection in Imbalanced Diplomatic Dialogues via Positive-Unlabeled Learning

arXiv: https://arxiv.org/abs/2507.09157 · HTML: https://mlsanity.com/paper/2507.09157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 12 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)

## Abstract (arXiv)

Detecting deception in strategic dialogues is a complex and high-stakes task due to the subtlety of language and extreme class imbalance between deceptive and truthful communications. In this work, we revisit deception detection in the Diplomacy dataset, where less than 5% of messages are labeled deceptive. We introduce a lightweight yet effective model combining frozen BERT embeddings, interpretable linguistic and game-specific features, and a Positive-Unlabeled (PU) learning objective. Unlike traditional binary classifiers, PU-Lie is tailored for situations where only a small portion of deceptive messages are labeled, and the majority are unlabeled. Our model achieves a new best macro F1 of 0.60 while reducing trainable parameters by over 650x. Through comprehensive evaluations and ablation studies across seven models, we demonstrate the value of PU learning, linguistic interpretability, and speaker-aware representations. Notably, we emphasize that in this problem setting, accurately detecting deception is more critical than identifying truthful messages. This priority guides our choice of PU learning, which explicitly models the rare but vital deceptive class.