Research on Intelligent Interaction Design for Enhancing Multi-Scenario English Learning Using ChatGPT and Deep Reinforcement Learning
Bibliographic record
Abstract
Human-computer interaction scenarios have a broad prospect in the field of English learning.In this paper, a human-computer dialogue interaction system for English learning scenarios is designed based on deep reinforcement learning and artificial intelligence interaction technology.Firstly, a speech enhancement method based on collaborative recurrent network is proposed to optimize the speech analysis module.On this basis, we design the framework of human-computer interaction system, and construct a human-computer dialogue interaction system for English learning scenarios that contains three modules: natural language understanding (NLU), knowledge retrieval enhancement, and natural language generation (NLG), in which knowledge retrieval enhancement utilizes ChatGPT for document reordering design.In the speech enhancement simulation experiments, the mean value of network congestion for the speech enhancement method designed in this paper is 0.073, which achieves at least 50% performance improvement, reduces speech distortion and optimizes the signal-to-noise ratio at the same time.The system is experimentally analyzed for two tasks, conversation state tracking and conversation reply generation, and outperforms the baseline model on both tasks.Finally, a subjective evaluation is conducted, and the system in this paper scores 3.766, which is obviously a smoother human-computer interaction experience, and the English learning interaction experience has a greater advantage compared with the other methods.This paper provides innovative ideas and feasible methods for combining cutting-edge information technology with interactive English teaching.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".