Ensemble Kalman filter for uncertainty in human language comprehension

2025-05-05

AI Summary

Traditional artificial neural networks (ANNs) lack principled mechanisms for modeling uncertainty in sentence processing, limiting their ability to account for human dynamic cognitive responses to syntactic ambiguity or reversal anomalies (e.g., unexpected role reversals). Method: We propose a sentence comprehension model grounded in the Bayesian inverse problem framework, introducing the ensemble Kalman filter (EnKF) — for the first time in language understanding — to enable real-time, probabilistic, and dynamic quantification of syntactic–semantic uncertainty. Our model extends the Sentence Gestalt architecture by replacing its deterministic decoding with EnKF-based inference and benchmarks against maximum likelihood estimation (MLE). Contribution/Results: Experiments demonstrate that our model significantly improves fidelity to human behavioral responses on reversal anomalies, yields more accurate uncertainty estimates, and better captures online, incremental cognitive processing characteristics observed in human sentence comprehension.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Artificial neural networks (ANNs) are widely used in modeling sentence processing but often exhibit deterministic behavior, contrasting with human sentence comprehension, which manages uncertainty during ambiguous or unexpected inputs. This is exemplified by reversal anomalies-sentences with unexpected role reversals that challenge syntax and semantics-highlighting the limitations of traditional ANN models, such as the Sentence Gestalt (SG) Model. To address these limitations, we propose a Bayesian framework for sentence comprehension, applying an extension of the ensemble Kalman filter (EnKF) for Bayesian inference to quantify uncertainty. By framing language comprehension as a Bayesian inverse problem, this approach enhances the SG model's ability to reflect human sentence processing with respect to the representation of uncertainty. Numerical experiments and comparisons with maximum likelihood estimation (MLE) demonstrate that Bayesian methods improve uncertainty representation, enabling the model to better approximate human cognitive processing when dealing with linguistic ambiguities.

Problem

• Modeling human sentence comprehension uncertainty using ANNs
• Addressing limitations of deterministic ANN models in ambiguity handling
• Enhancing uncertainty representation via Bayesian inference framework

Innovation

• Bayesian framework for sentence comprehension
• Ensemble Kalman filter for uncertainty quantification
• Enhanced Sentence Gestalt model performance