Source-linked AI summary
Experience Grounds Language
Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
TL;DR
Language understanding remains limited when models learn from text without the physical and social experience that gives utterances meaning. The paper surveys progressively broader World Scopes and argues for grounding language in perception, embodiment, and social participation. It concludes that future tasks, representations, and inductive biases should broaden models’ access to human experience.
Problem
Language understanding lacks sufficient connection to the physical world and social interactions that support meaningful communication.
Method
The paper organizes prior and emerging research into progressively broader World Scopes spanning written language, perception, embodiment, and social interaction.
Results
Text-based representations capture rich syntax, semantics, and transferable associations, but text-only systems still miss grounded features and experience-informed inferences.
Takeaways & Limitations
Language research should incrementally contextualize models in human experience through grounding and agency across broader physical and social scopes.
Takeaways & Limitations
Current evaluation paradigms make it difficult to test persistent agents that participate consistently and learn from temporally structured, non-independent experience.
Abstract
from arXiv · showhide
Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.
1 WS1: Corpora and Representations
WS1 traces NLP’s foundations from annotated corpora and formal structure toward distributed representations learned from larger contexts. These representations capture substantial syntactic and semantic information, but their interpretability and imposed structure remain recurring concerns.
- Annotated corpora such as the Penn Treebank initially supported research on formal linguistic structure, including syntax-tree recovery.
- Neural sequence models showed that vector representations could capture both syntax and semantics without relying exclusively on explicit annotations.
- Context-based representations range from interpretable hierarchies and word classes to low-dimensional vectors learned from co-occurrence patterns.Hierarchical models impose structure, while LSI/A and LDA use broader document context and latent topics.
- Connectionist research shifted words away from intrinsically interpretable symbols toward distributed representations, preserving a longstanding localist-versus-distributed debate.
- Larger unstructured corpora broadened context and induced richer semantics than earlier distributional settings were believed to support.
2 WS2: The Written World
WS2 expands NLP from curated corpora to massive, unstructured written-world data, producing strong transferable representations and benchmark gains. Yet text-only modeling remains bounded by diminishing returns and missing experience-informed, grounded inferences.
- Web-scale corpora broadened NLP’s world scope to unstructured, unlabeled, multilingual writing, but remained limited to what humanity has written.
- Large-scale raw data produced substantial gains on existing and novel benchmarks, while learned representations transferred knowledge across diverse domains.
- Text representations require scale in both data and parameters, with models growing from approximately 10^8 to 10^11 parameters.
- Text-only language modeling still searches symbolic co-occurrences without directly grounding them in meaning or the physical world.
- On LAMBADA, a 175B-parameter model gained 8 percentage-points while leaving 25% unsolved, illustrating diminishing returns from further scaling.
- Sentence and document context captures semantic relations and sentence-level inferences, but pretrained representations still miss grounded features and difficult experience-informed coreference.
- The paper posits multimodal perception as additional supervision for learning remaining aspects of meaning in context as text pretraining approaches diminishing returns.
3 WS3: The World of Sights and Sounds
WS3 argues that language learning must incorporate auditory, tactile, and visual perception because sensory experience supplies semantic knowledge absent from text. Multimodal and vision-language resources provide routes toward richer grounding, reasoning, and long-tail understanding.
- Perception supplies semantic axioms, metaphors, reasoning knowledge, and reporting context, while evidence indicates children require grounded sensory experience beyond speech.
- Auditory, tactile, and visual inputs encode prosody, physical properties, and experiences that text alone cannot document.
- Text-only guides cannot provide the fundamental referents needed to understand activities and their causal, physical, and social implications.
- Computer vision’s reusable infrastructure and semantic representations have enabled interaction with natural language beyond simple visual labels.
- Text-vision benchmarks and multimodal transformers combine language with images and, increasingly, audio for captioning, translation, and related tasks.
- Video-rich WS3 agents are expected to reduce reporting bias and improve long-tail generalization, especially in zero-shot physical reasoning.
4 WS4: Embodiment and Action
WS4 extends language grounding from perception to situated action, where agents manipulate environments, learn physical properties, and translate language into behavior. Embodiment offers richer pre-linguistic representations but remains constrained by limited robotic utility and infrastructure.
- Interactive multimodal experience forms action-oriented categories, and language grounding requires action to fully discover their connections.
- Embodied agents in virtual, simulated, or real environments translate language into action while actively learning about the world.
- Compared with text and perception, embodied agents can reason over richer object affordances involving texture, weight, deformability, and manipulation.
- Interaction supports learning physical properties, pre-linguistic representations, and abstraction through trial-and-error planning.
- Embodiment supplies intuitive physical knowledge needed to understand metaphors and other language grounded in spatial and material realities.
- Current representations have very limited utility in basic robotic settings, while robotics and embodiment lack the off-the-shelf availability of computer-vision models.
5 WS5: The Social World
The social world gives language meaning through interpersonal purposes, identities, and context. The paper argues that realistic language learning therefore requires interactive settings where agents can develop and use persistent social knowledge.
- Social meaning: Interpersonal communication is a foundational use of natural language because utterances come from sources with purposes and intentions.The “BULL” example requires inferring both the warning’s social purpose and the creature’s location.
- Social meaning: Theory of Mind models the feelings and knowledge of other people, supporting communication with agents who have their own desires and identities.The paper also connects this paradigm to Speaker-Listener models and Rational Speech Act models.
- Evaluation: Static datasets risk spurious patterns and bias because real interactions include disagreement, persistent identities, and changing circumstances.Dynamic evaluation helps partially, but identity persistence and adaptation remain unresolved.
- Learning by participation: Current training data lacks discriminatory signals for efficiently hypothesizing consistent identities or mental states, while cross-entropy losses suppress infrequent events.These limitations conflict with the goal of emulating human zero-shot decisions from past experience.
- Evaluation: Ecologically valid evaluation requires situations where artificial agents have enough identity to receive social standing in interactions.Crowd-consensus labels commonly omit the status, role, and intention embedded in concrete social contexts.
- Learning by participation: Learning by participation lets users interact freely with agents, exposing how varying signals and attributes construct sociolinguistic identity.Such interaction also enables probing simplifications of reality and missing commonsense knowledge.
6 Self-Evaluation
The paper introduces World Scopes as a framework for making concrete claims about the contextual foundations of language learning.
- World Scopes provide the framework used to formulate the paper’s concrete claims.
- The paper uses World Scopes to organize claims about language learning beyond text.
- The framework is presented through claims intended to make its distinctions concrete.
You can’t learn language ...
The paper argues that language learning must extend beyond text to perception, embodiment, and social interaction. It proposes research settings and tasks that ground language in physical experience, action, and participation.
- World Scope criteria: A learner is not in WS3 if it can succeed without perception, including visual or auditory input.
- World Scope criteria: A learner is not in WS4 if its possible world actions and consequences can be enumerated.
- World Scope criteria: A learner is not in WS5 unless achieving its goals requires cooperation with a human in the loop.
- World Scope criteria: Most NLP research remains in WS2, targeting a different goal from language learning without thereby losing its utility.
- Research directions: Moving beyond WS2 requires systems that represent meaning, reasoning, causality, uncertainty, and long-term goals.
- Research directions: Grounded tasks include coreference and word-sense disambiguation with shared scenes, tactile word learning, and socially situated sentiment.These tasks connect language to listeners’ experiences, object function, gestures, and personally charged contexts.
7 Conclusions
The paper calls for purposeful contextualization of language in human experience and broader World Scopes for models. It identifies persistent, time-structured participation as a route toward more human-like generalization.
- WS5 requires persistent agents with personal experiences over time, unlike the IID datasets that confine most machine-learning models.
- The paper calls for incremental contextualization of language in human experience and asks which tasks, representations, and inductive biases fill current gaps.
- Computer vision, speech recognition, robotics, simulators, and videogames provide environments for expanding models toward WS3–WS5.The paper urges the community to prioritize grounding and agency.