In recent years, converging evidence has suggested that prediction plays a role in language comprehension, as it appears to do in information processing in a range of cognitive domains. Much of the evidence for this comes from the N400, a neural index of the processing of meaningful stimuli which has been argued to index the extent to which a word…

In this article, we investigate a systematic workflow that supports the learning engineering process of formulating the starting question for a conversational module based on existing learning materials, specifying the input that transformer-based language models need to function as classifiers, and specifying the adaptive dialogue structure,…

This dissertation features a relative clause island, whose status has been known to differ significantly across languages and extraction types. By conducting a series of acceptability judgment experiments with human participants, as well as measuring token-by-token surprisal values among large language models, I demonstrate the following: First,…

In this thesis, I use a combination of simulations and empirical data to demonstrate that a small set of structural and functional principles - the basic tenets of predictive coding theory - succinctly accounts for a very wide range of properties in the language processing system. Predictive coding approximates hierarchical Bayesian inference via…

Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…

Purpose: This paper aims to critically review the intersection of searching and learning among children in the context of voice-based conversational agents (VCAs). This study presents the opportunities and challenges around reconfiguring current VCAs for children to facilitate human learning, generate diverse data to empower VCAs, and assess…

In this dissertation, I establish a research program that uses computational modeling as a testbed for theories of phonological learning. This dissertation focuses on a fundamental question: how do children acquire sound patterns from noisy, real-world data, especially in the presence of lexical exceptions that defy regular patterns? For instance,…

In this dissertation, I investigate how people navigate ambiguity in everyday speech, with a focus on quantifier-negation sentences. Combining corpus analysis, behavioral experiments, and computational modeling in the Rational Speech Act framework, I explore preferred interpretations of quantifier-negation and examine the contexts and prosodies…

Speakers display considerable variability in language use and representations: they may have different pronunciations of the same word, different intended meanings for the same phrases, and different sets of syntactic constraints in their internalized grammars. Comprehenders adapt to such variability by constantly updating their expectations for…

This study applies the Apriori algorithm to analyse patterns, syntactic structures, and thematic clusters in Chinese studies data from various genres. This study aims to identify recurring linguistic elements in order to shed light on the dynamic nature of the Chinese language across different contexts and time periods. The Apriori algorithm is…

Technological advances have made the ability to translate no longer exclusively belong to humans. Today, machine translation has turned into a tool with superior performance to convert text between languages without the need for human intervention. One of the translation research foci is the studies of causative translation, especially from…

The world's linguistic diversity is severely underrepresented in research on cognitive and neural aspects of language processing, with great consequences for our understanding of the relationship between language, cognition, and the human brain. The practical challenges of carrying out neurophysiological (but also behavioral) experiments under…

Learning analytics (LA) involves the measurement, collection, analysis, and reporting of data about learners and their contexts, aiming to understand and optimize both the learning process and the environments in which it occurs. Among many themes that the LA community considers, natural language processing (NLP) algorithms have been widely…

As language technologies have become more sophisticated and prevalent, there have been increasing concerns about bias in natural language processing (NLP). Such work often focuses on the effects of bias instead of sources. In contrast, this paper discusses how normative language assumptions and ideologies influence a range of automated language…

Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…