# Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting

arXiv: https://arxiv.org/abs/2608.25359 · HTML: https://mlsanity.com/paper/2608.25359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://mlsanity.com/paper/2004.06871/) (2020)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop](https://mlsanity.com/paper/1909.01362/) (2019)
- [Dynamic Forecasting of Conversation Derailment](https://mlsanity.com/paper/2110.05111/) (2021)
- [DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://mlsanity.com/paper/2306.09030/) (2023)
- [Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text Corpora](https://mlsanity.com/paper/2211.16259/) (2022)
- [A Graph-Based Context-Aware Model to Understand Online Conversations](https://mlsanity.com/paper/2211.09207/) (2022)
- [Don't Let Me Be Misunderstood: Comparing Intentions and Perceptions in Online Discussions](https://mlsanity.com/paper/2004.13609/) (2020)
- [How Did We Get Here? Summarizing Conversation Dynamics](https://mlsanity.com/paper/2404.19007/) (2024)
- [Target Based Speech Act Classification in Political Campaign Text](https://mlsanity.com/paper/1905.07856/) (2019)
- [Understanding and Predicting Derailment in Toxic Conversations on GitHub](https://mlsanity.com/paper/2503.02191/) (2025)
- [Conversations Gone Awry, But Then? Evaluating Conversational Forecasting Models](https://mlsanity.com/paper/2507.19470/) (2025)

## Abstract (arXiv)

Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing approaches often struggle in low-data settings and to generalize across domains. This poses a challenge for new platforms and smaller communities where annotated data is limited. We propose modeling pragmatic representations of conversations to reduce lexical noise and improve generalizability. Specifically, speech act information is used as an auxiliary learning signal alongside textual semantics. Experimental results show improved performance across three datasets, particularly in low-data and cross-domain settings.