| Challenge: | A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between word forms and their meaning, or does some systematic phenomenon pervade? |
| Approach: | They propose to quantify the systematicity of the sign using mutual information and recurrent neural networks to examine 106 languages. |
| Outcome: | The proposed model reduces entropy in a word form conditioned on its semantic representation and recovers English examples of systematic affixes. |
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Systematicity between Forms and Meanings across Languages Supports Efficient Communication (2026.acl-long)
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| Challenge: | Languages vary in how meanings map to word forms, but this theory does not account for systematic relations within word forms. |
| Approach: | They propose a model that measures the learnability of meaning-to-form mappings by inverse of simplicity. |
| Outcome: | The proposed model captures fine-grained regularities in linguistic form, allowing better discrimination between attested and unattested systems. |
Probing Linguistic Systematicity (2020.acl-main)
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| Challenge: | Existing evidence that deep natural language understanding models do not learn systematically is lacking. |
| Approach: | They examine whether deep natural language understanding models exhibit systematicity . they find that network architectures can generalize non-systematically . |
| Outcome: | The proposed model generalizes non-systematically, but is unsatisfactory, the authors argue . they show that the current state-of-the-art models do not generalize systematically . |
What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language (2020.coling-main)
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| Challenge: | Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. |
| Approach: | They propose that compositionality can be measured by measuring meaning-form correlation . they analyze three sets of languages: artificial toy languages tailored to be compositional . |
| Outcome: | The proposed method can assess compositionality on three sets of languages . linguistic phenomena such as synonymy and ungrounded stop-words weigh on the results . |
A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)
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| Challenge: | Symbolic meaning representations of natural language text have been studied since at least the 1960s . with the availability of large annotated corpora, the field has recently seen several new developments . |
| Approach: | They propose a framework for expressing meaning in natural language text using annotated corpora and a set of tools for machine learning. |
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Finding Concept-specific Biases in Form–Meaning Associations (2021.naacl-main)
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| Challenge: | Existing methods to detect cross-linguistic associations are not effective, but their effects are minor. |
| Approach: | They propose a method to measure cross-linguistic associations by controlling for the influence of language family and geographic proximity within a large concept-aligned, cross-lingual lexicon. |
| Outcome: | The proposed method shows that it is small, but it is unsurprisingly small (less than 0.5% on average). |
Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (2020.acl-main)
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| Challenge: | a priori, large neural language models are described as understanding or capturing meaning on tasks that are ostensibly meaningsensitive. |
| Approach: | They argue that a system trained only on form has no way to learn meaning . they argue that this is due to a misunderstanding of the relationship between form and meaning - which is a misconception in NLP . |
| Outcome: | The proposed model can't learn meaning because it only uses form as training data, the authors argue . they argue that a clear understanding of the distinction between form and meaning will guide the field towards better science around natural language understanding. |
Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey (2025.acl-long)
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| Challenge: | Existing benchmarks and models focus on systematicity of representations, but they focus on the systematicity in behaviour. |
| Approach: | They argue that systematicity is a desirable property in ML models as it enables strong generalization to novel contexts. |
| Outcome: | The proposed benchmarks and models focus on the systematicity of behaviour, while existing models focus primarily on language and vision. |
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective (2022.findings-acl)
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| Challenge: | Existing studies focus on robustness-like metamorphic relations, which limit the scope of linguistic properties they can test. |
| Approach: | They propose three new classes of metamorphic relations which address the properties of systematicity, compositionality and transitivity. |
| Outcome: | The proposed methods show that metamorphic models do not always behave according to expected linguistic properties. |
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
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| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
| Approach: | This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics . |
| Outcome: | This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics . |
Systematic Generalization in Language Models Scales with Information Entropy (2025.findings-acl)
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| Challenge: | Existing benchmarks for assessing compositional behavior are unclear on how to measure the difficulty of a systematic generalization problem. |
| Approach: | They propose a framework for measuring entropy in a sequence-to-sequence task and a method for measuring it. |
| Outcome: | The proposed framework scales with the entropy of the distribution of component parts in the training data. |