Developing a Task-Based Dialogue System for English Language Learning

Abstract

This research involved the design of a task-based dialogue system and evaluation of its learning effectiveness. Dialogue training still heavily depends on human communication with instant feedback or correction. However, it is not possible to provide a personal tutor for every English learner. With the rapid development of information technology, digitized learning and voice communication is a possible solution. The goal of this research was to develop an innovative model to refine the task-based dialogue system, including natural language understanding, disassembly intention, and dialogue state tracking. To enable the dialogue system to find the corresponding sentence accurately, the dialogue system was designed with machine learning algorithms to allow users to communicate in a task-based fashion. Past research has pointed out that computer-assisted instruction has achieved remarkable results in language reading, writing, and listening. Therefore, the direction of the discussion is to use the task-oriented dialogue system as a speaking teaching assistant. To train the speaking ability, the proposed system provides a simulation environment with goal-oriented characteristics, allowing learners to continuously improve their language fluency in terms of speaking ability by simulating conversational situational exercises. To evaluate the possibility of replacing the traditional English speaking practice with the proposed system, a small English speaking class experiment was carried out to validate the effectiveness of the proposed system. Data of 28 students with three assigned tasks were collected and analyzed. The promising results of the collected students’ feedback confirm the positive perceptions toward the system regarding user interface, learning style, and the system’s effectiveness.

Research on Oral English Dialogue Understanding Based on Deep Learning

“…RNNs, an important model in the field of NLP, were used by [22] to build and perform the task of intention recognition. e authors of [23] compared and investigated traditional recurrent neural networks with the above two variants of the structure on sentence classification tasks and demonstrated that long and short-term memory networks and recurrent networks of gate units were significantly better than ordinary recurrent neural networks. e authors of [10] used recurrent neural networks for the first time for the slot filling task and obtained good results.…”

“…e ATIS data include 4978 sentences in the training set from the ATIS-2 and ATIS-3 datasets of Class A data and 893 sentences in the test set from the Nov93 and Scientific Programming Dec94 datasets of ATIS-3, where the words in each sentence can be found in a table of named corpora, including domain-specific entities such as city, airline, airport name, and date [23]. e ATIS database uses a semantic framework to represent that each sentence has an intention and each word in the sentence has a corresponding semantic slot.…”

The use of artificially intelligent chatbots in English language learning: A systematic meta-synthesis study of articles published between 2010 and 2024

“…Another theme regarding the pedagogy of AI chatbots in English language learning was the design methodology of pedagogical activities in the reviewed studies. Our review illustrated that researchers used the principles of Ellis's (2003) task-based language teaching using tasks in speaking scenarios for language practice in designing AI chatbots (Ayedoun et al, 2019a(Ayedoun et al, , 2020Li, Chang & Wu, 2020;Sydorenko, Daurio & Thorne, 2018;Sydorenko et al, 2019). To illustrate, in a study using HALEF, the users practiced speaking English in a coffee shop simulation.…”

Integration of Chatbots in Additional Language Education: A Systematic Review

“…Chen et al (2023) emphasize increased engagement, self-efficacy, and satisfaction due to technology-enhanced learning, with feedback playing a crucial role. Many studies indicate that students are more likely to engage in well-structured speaking practices embedded in context and linked to a dialogue system such as chatbots (Li et al, 2020), as indicated in their mixed-method study conducted on beginner level learners.…”