# Why Are Parsing Actions for Understanding Message Hierarchies Not Random?

arXiv: https://arxiv.org/abs/2506.22366 · HTML: https://mlsanity.com/paper/2506.22366/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020)
- [Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols](https://mlsanity.com/paper/1705.11192/) (2017)
- [Compositionality and Generalization in Emergent Languages](https://mlsanity.com/paper/2004.09124/) (2020)
- [Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input](https://mlsanity.com/paper/1804.03984/) (2018)
- [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://mlsanity.com/paper/2504.08165/) (2025)
- [Anti-efficient encoding in emergent communication](https://mlsanity.com/paper/1905.12561/) (2019)
- [Learning to Transduce with Unbounded Memory](https://mlsanity.com/paper/1506.02516/) (2015)
- [Learning Hierarchical Priors in VAEs](https://mlsanity.com/paper/1905.04982/) (2019)
- [Emergent Communication: Generalization and Overfitting in Lewis Games](https://mlsanity.com/paper/2209.15342/) (2022)
- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [Emergent Language: A Survey and Taxonomy](https://mlsanity.com/paper/2409.02645/) (2024)
- [Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments](https://mlsanity.com/paper/2311.04453/) (2023)

## Abstract (arXiv)

If humans understood language by randomly selecting parsing actions, it might have been necessary to construct a robust symbolic system capable of being interpreted under any hierarchical structure. However, human parsing strategies do not seem to follow such a random pattern. Why is that the case? In fact, a previous study on emergent communication using models with hierarchical biases have reported that agents adopting random parsing strategiesunicodex2013ones that deviate significantly from human language comprehensionunicodex2013can achieve high communication accuracy. In this study, we investigate this issue by making two simple and natural modifications to the experimental setup: (I) we use more complex inputs that have hierarchical structures, such that random parsing makes semantic interpretation more difficult, and (II) we incorporate a surprisal-related term, which is known to influence the order of words and characters in natural language, into the objective function. With these changes, we evaluate whether agents employing random parsing strategies still maintain high communication accuracy.