# Did they answer? Subjective acts and intents in conversational discourse

arXiv: https://arxiv.org/abs/2104.04470 · HTML: https://mlsanity.com/paper/2104.04470/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://mlsanity.com/paper/2010.03532/) (2020)
- [Towards Debiasing Fact Verification Models](https://mlsanity.com/paper/1908.05267/) (2019)
- [Exploring the Role of Prior Beliefs for Argument Persuasion](https://mlsanity.com/paper/1906.11301/) (2019)
- [Dialogue Act Recognition via CRF-Attentive Structured Network](https://mlsanity.com/paper/1711.05568/) (2017)
- [Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements](https://mlsanity.com/paper/1901.11429/) (2019)
- [Don't Let Me Be Misunderstood: Comparing Intentions and Perceptions in Online Discussions](https://mlsanity.com/paper/2004.13609/) (2020)

## Built on by (most cited first, 22 of 22)

- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [Analyzing Dataset Annotation Quality Management in the Wild](https://mlsanity.com/paper/2307.08153/) (2023)
- [You Are What You Annotate: Towards Better Models through Annotator Representations](https://mlsanity.com/paper/2305.14663/) (2023)
- [Investigating Reasons for Disagreement in Natural Language Inference](https://mlsanity.com/paper/2209.03392/) (2022)
- [Learning with Different Amounts of Annotation: From Zero to Many Labels](https://mlsanity.com/paper/2109.04408/) (2021)
- [ALLSH: Active Learning Guided by Local Sensitivity and Hardness](https://mlsanity.com/paper/2205.04980/) (2022)
- [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://mlsanity.com/paper/2304.00815/) (2023)
- [Using Natural Language Explanations to Rescale Human Judgments](https://mlsanity.com/paper/2305.14770/) (2023)
- [D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation](https://mlsanity.com/paper/2404.10857/) (2024)
- [PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization](https://mlsanity.com/paper/2407.18078/) (2024)
- ["I Never Said That": A dataset, taxonomy and baselines on response clarity classification](https://mlsanity.com/paper/2409.13879/) (2024)
- [Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations](https://mlsanity.com/paper/2402.01423/) (2024)
- [SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions](https://mlsanity.com/paper/2603.14027/) (2026)
- [Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation](https://mlsanity.com/paper/2402.14101/) (2024)
- [Designing NLP Systems That Adapt to Diverse Worldviews](https://mlsanity.com/paper/2405.11197/) (2024)
- [Sources of Noise in Dialogue and How to Deal with Them](https://mlsanity.com/paper/2212.02745/) (2022)
- [Self-Knowledge Distillation for Learning Ambiguity](https://mlsanity.com/paper/2406.09719/) (2024)
- [C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset](https://mlsanity.com/paper/2509.21548/) (2025)
- [Learning to vary: Teaching LMs to reproduce human linguistic variability in next-word prediction](https://mlsanity.com/paper/2509.17794/) (2025)
- [Human Label Variation in Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2602.22723/) (2026)
- [Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions](https://mlsanity.com/paper/2604.20168/) (2026)
- [PolERo: Studying Political Evasion in Romanian](https://mlsanity.com/paper/2609.02391/) (2026)

## Abstract (arXiv)

Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences. At the same time, discourse is embedded in a social context, meaning that interpreters apply their own assumptions and beliefs when resolving these inferences, leading to multiple, valid interpretations. However, current discourse data and frameworks ignore the social aspect, expecting only a single ground truth. We present the first discourse dataset with multiple and subjective interpretations of English conversation in the form of perceived conversation acts and intents. We carefully analyze our dataset and create computational models to (1) confirm our hypothesis that taking into account the bias of the interpreters leads to better predictions of the interpretations, (2) and show disagreements are nuanced and require a deeper understanding of the different contextual factors. We share our dataset and code at http://github.com/elisaF/subjective_discourse.