Language Cognition and Language Computation -- Human and Machine Language Understanding

TL;DR

This paper reviews and compares how cognitive science and computer science approach language understanding, exploring their differences, overlaps, and potential for interdisciplinary insights to advance intelligent language models.

Contribution

It provides a comprehensive comparison of research questions, methods, and progress in language understanding across cognitive and computer sciences, highlighting interdisciplinary opportunities.

Findings

Cognitive science focuses on brain mechanisms of language.

Computer science emphasizes practical application efficiency.

Integrative approaches can enhance language model development.

Abstract

Language understanding is a key scientific issue in the fields of cognitive and computer science. However, the two disciplines differ substantially in the specific research questions. Cognitive science focuses on analyzing the specific mechanism of the brain and investigating the brain's response to language; few studies have examined the brain's language system as a whole. By contrast, computer scientists focus on the efficiency of practical applications when choosing research questions but may ignore the most essential laws of language. Given these differences, can a combination of the disciplines offer new insights for building intelligent language models and studying language cognitive mechanisms? In the following text, we first review the research questions, history, and methods of language understanding in cognitive and computer science, focusing on the current progress and challenges. We then compare and contrast the research of language understanding in cognitive and computer sciences. Finally, we review existing work that combines insights from language cognition and language computation and offer prospects for future development trends.