# Pragmatically Informative Text Generation

arXiv: https://arxiv.org/abs/1904.01301 · HTML: https://mlsanity.com/paper/1904.01301/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 30 of 32)

- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [FUDGE: Controlled Text Generation With Future Discriminators](https://mlsanity.com/paper/2104.05218/) (2021)
- [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://mlsanity.com/paper/2112.08726/) (2021)
- [Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning](https://mlsanity.com/paper/2107.02794/) (2021)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://mlsanity.com/paper/2109.08828/) (2021)
- [Inducing Positive Perspectives with Text Reframing](https://mlsanity.com/paper/2204.02952/) (2022)
- [From partners to populations: A hierarchical Bayesian account of coordination and convention](https://mlsanity.com/paper/2104.05857/) (2021)
- [Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions](https://mlsanity.com/paper/2311.00233/) (2023)
- [Modeling Cross-Cultural Pragmatic Inference with Codenames Duet](https://mlsanity.com/paper/2306.02475/) (2023)
- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://mlsanity.com/paper/2211.08371/) (2022)
- [Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG](https://mlsanity.com/paper/2109.07043/) (2021)
- [RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework](https://mlsanity.com/paper/2410.19109/) (2024)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Search and Learn: Improving Semantic Coverage for Data-to-Text Generation](https://mlsanity.com/paper/2112.02770/) (2021)
- [A practical introduction to the Rational Speech Act modeling framework](https://mlsanity.com/paper/2105.09867/) (2021)
- [GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews](https://mlsanity.com/paper/2406.07359/) (2024)
- [Evaluating Pragmatic Abilities of Image Captioners on A3DS](https://mlsanity.com/paper/2305.12777/) (2023)
- [Pragmatic Inference with a CLIP Listener for Contrastive Captioning](https://mlsanity.com/paper/2306.08818/) (2023)
- [Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking](https://mlsanity.com/paper/2307.14440/) (2023)
- [LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes](https://mlsanity.com/paper/2402.06930/) (2024)
- [PROGrasp: Pragmatic Human-Robot Communication for Object Grasping](https://mlsanity.com/paper/2309.07759/) (2023)
- [DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation](https://mlsanity.com/paper/2311.04766/) (2023)
- [Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog](https://mlsanity.com/paper/2507.14063/) (2025)
- [R2D2: Relational Text Decoding with Transformers](https://mlsanity.com/paper/2105.04645/) (2021)
- [For Generated Text, Is NLI-Neutral Text the Best Text?](https://mlsanity.com/paper/2302.08577/) (2023)
- [Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation](https://mlsanity.com/paper/2407.14088/) (2024)
- [Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval](https://mlsanity.com/paper/2505.03676/) (2025)
- [Informative Text Generation from Knowledge Triples](https://mlsanity.com/paper/2209.12733/) (2022)
- [Self-training from Self-memory in Data-to-text Generation](https://mlsanity.com/paper/2401.10567/) (2024)
- [TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy](https://mlsanity.com/paper/2402.01714/) (2024)

## Abstract (arXiv)

We improve the informativeness of models for conditional text generation using techniques from computational pragmatics. These techniques formulate language production as a game between speakers and listeners, in which a speaker should generate output text that a listener can use to correctly identify the original input that the text describes. While such approaches are widely used in cognitive science and grounded language learning, they have received less attention for more standard language generation tasks. We consider two pragmatic modeling methods for text generation: one where pragmatics is imposed by information preservation, and another where pragmatics is imposed by explicit modeling of distractors. We find that these methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations.