[Paper Review] Unsupervised Language Acquisition

Carl G. de Marcken|ArXiv.org|Nov 12, 1996

Algorithms and Data CompressionComputer Science114 references129 citations

TL;DR

This paper presents a computational theory of unsupervised language acquisition that models language learning as statistical inference over a stochastic, generative grammar. By using a compositional representation of linguistic parameters and a content-based learning algorithm that separates grammar content from representation, the approach achieves high accuracy in learning lexicons, stochastic grammars, and meaning mappings from unsegmented speech and text—matching human-annotated linguistic structures with minimal supervision.

ABSTRACT

This thesis presents a computational theory of unsupervised language acquisition, precisely defining procedures for learning language from ordinary spoken or written utterances, with no explicit help from a teacher. The theory is based heavily on concepts borrowed from machine learning and statistical estimation. In particular, learning takes place by fitting a stochastic, generative model of language to the evidence. Much of the thesis is devoted to explaining conditions that must hold for this general learning strategy to arrive at linguistically desirable grammars. The thesis introduces a variety of technical innovations, among them a common representation for evidence and grammars, and a learning strategy that separates the ``content'' of linguistic parameters from their representation. Algorithms based on it suffer from few of the search problems that have plagued other computational approaches to language acquisition. The theory has been tested on problems of learning vocabularies and grammars from unsegmented text and continuous speech, and mappings between sound and representations of meaning. It performs extremely well on various objective criteria, acquiring knowledge that causes it to assign almost exactly the same structure to utterances as humans do. This work has application to data compression, language modeling, speech recognition, machine translation, information retrieval, and other tasks that rely on either structural or stochastic descriptions of language.

Motivation & Objective

• To develop a principled, unsupervised computational model of how children learn language from unsegmented, unannotated input without explicit feedback.
• To minimize assumptions about the learning environment, particularly avoiding reliance on semantic knowledge or labeled data.
• To design a learning mechanism that infers grammatical structure by fitting a stochastic, generative model to observed linguistic evidence.
• To enable learning of lexicons, grammars, and meaning representations from continuous speech and text using only statistical regularities.
• To create a framework that balances linguistic plausibility with statistical optimality through a description length-based learning criterion.

Proposed method

• Uses a compositional representation where both utterances and grammar parameters are built by composing simpler elements, enabling multi-scale pattern capture.
• Employs a stochastic, generative model of language where the goal is to find a grammar under which the observed input is statistically typical.
• Introduces a learning strategy that manipulates the 'content' of grammar parameters rather than their explicit representation, avoiding local optima in search space.
• Applies the Minimum Description Length (MDL) principle to balance model complexity and fit to data, favoring grammars that compress the input well.
• Uses perturbation operators on meaning representations to explore linguistic structure, enabling learning of compositional and non-compositional patterns.
• Implements algorithms that make multiple passes over input data, optimizing grammar parameters based on statistical likelihood and description length.

Experimental results

Research questions

• RQ1 How can a learner acquire a grammatical structure from unsegmented, unannotated speech or text without any explicit supervision?
• RQ2 What conditions must be met for a statistical learning procedure to converge on a linguistically plausible grammar?
• RQ3 Can a grammar be learned that captures both phonological, lexical, and syntactic regularities using only input frequency and distributional patterns?
• RQ4 How can the representation of linguistic parameters be designed to support efficient learning and generalization across multiple linguistic scales?
• RQ5 To what extent can meaning representations be inferred from cross-linguistic or parallel text data using unsupervised learning?

Key findings

• The model successfully learns lexicons and stochastic grammars from unsegmented text with performance approaching human-annotated linguistic structure on objective criteria.
• The learning algorithm achieves high accuracy in mapping between sound and meaning representations, even without explicit semantic supervision.
• The compositional parameter representation enables the model to capture patterns at multiple levels of linguistic abstraction simultaneously.
• The content-based learning strategy avoids common search problems in grammar induction by decoupling grammar content from its syntactic form.
• The framework supports learning from continuous speech signals, with promising but preliminary results suggesting potential for practical speech recognizer lexicon acquisition.
• The model demonstrates robustness to input noise and underdetermined parameters, supporting the feasibility of unsupervised acquisition in real-world conditions.