Papers by Guanliang Chen
Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring? (2022.naacl-main)
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| Challenge: | DL-based graders often lack the ability to explain and justify how a prediction is made, which decreases their trustworthiness and hinders educators from embracing them in practice. |
| Approach: | They conducted a user study to determine whether DL-based graders align with human grader . they also ran a randomized controlled experiment to explore the impact of highlighting important words detected by DL grader. |
| Outcome: | The proposed method enables human graders to identify important words when marking short answer questions. |
KVFKT: A New Horizon in Knowledge Tracing with Attention-Based Embedding and Forgetting Curve Integration (2025.coling-main)
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| Challenge: | Existing knowledge tracing models do not incorporate forgetting features to improve the learning and answering processes. |
| Approach: | They propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration using four real-world datasets to test the model. |
| Outcome: | The proposed model outperforms the existing knowledge tracing models and eliminates the need for artificial engineering features. |
Bigger Data or Fairer Data? Augmenting BERT via Active Sampling for Educational Text Classification (2022.coling-1)
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| Challenge: | Pretrained Language Models (PLMs) encode bias against protected groups in the representations they learn, which may harm the prediction fairness of downstream models. |
| Approach: | They propose to quantify the awareness that a pretrained language model (BERT) has regarding people’s protected attributes and augment it to enhance prediction fairness of downstream models. |
| Outcome: | The proposed method improves fairness and accuracy of models by inhibiting the awareness of protected attributes in the PLMs. |