Automatic Cloze Distractor Generation with Pre-trained Language Models

แนวคิดหลัก

Using pre-trained language models for cloze distractor generation significantly improves performance.

บทคัดย่อ

Automatically generating cloze test distractors can enhance learner ability assessment by improving the effectiveness of the test. This paper explores using pre-trained language models (PLMs) to generate distractors, resulting in a substantial performance improvement. The CDGP framework incorporates training and ranking strategies to boost PLM-based distractor generation. Evaluation using benchmarking datasets shows significant outperformance compared to previous methods, advancing NDCG@10 score from 19.31 to 34.17, an improvement of up to 177%. The study also includes related work on cloze distractor generation methods and methodology details on candidate set generation and distractor selection.

สถิติ

Our best performing model advances the state-of-the-art result from 14.94 to 34.17 (NDCG@10 score).
The dataset consists of passages with cloze stems, answers, and distractors.
The CLOTH dataset statistics include average number of sentences and words per passage.
Different pre-trained language models used in experiments include BERT, SciBERT, RoBERTa, and BART.
Evaluation metrics include Precision (P@1), F1 score (F1@3, F1@10), Mean Reciprocal Rank (MRR@10), and Normalized Discounted Cumulative Gain (NDCG@10).

คำพูด

"Manually designing cloze test consumes enormous time and efforts."
"The major challenge lies in wrong option (distractor) selection."
"Our CDGP significantly outperforms the state-of-the-art result."

ข้อมูลเชิงลึกที่สำคัญจาก

CDGP

by Shang-Hsuan ... ที่ arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10326.pdf

สอบถามเพิ่มเติม

How can the difficulty level of automatically generated distractors be controlled effectively?

Controlling the difficulty level of automatically generated distractors is crucial for ensuring that they align with the intended assessment goals. One effective way to control the difficulty level is through fine-tuning the pre-trained language models (PLMs) used in generating distractors. By adjusting parameters during training, such as learning rate and input length, one can influence how challenging or easy the generated distractors are.
Additionally, incorporating features like word embedding similarity and contextual sentence embedding similarity in the Distractor Selector (DS) stage can help assess and adjust the difficulty level of distractors. These features provide insights into how closely related a distractor is to both the answer and context, allowing for more nuanced control over their complexity.
Moreover, implementing a feedback loop mechanism where human evaluators provide ratings on the perceived difficulty of generated distractors can be valuable. This feedback can then be used to iteratively refine the generation process and optimize for desired levels of challenge in line with educational objectives.

What are implications of using domain-specific PLMs for improving generation quality?

Utilizing domain-specific Pre-trained Language Models (PLMs) offers significant advantages in enhancing generation quality within specific subject areas or domains. When applied to cloze test preparation or any text-related tasks within a specialized field like science or literature, domain-specific PLMs bring several key benefits:

Domain Relevance: Domain-specific PLMs are trained on data from particular fields, making them more attuned to relevant terminology, concepts, and writing styles within that domain. This results in more accurate predictions and better-quality outputs tailored to specific subject matter.

Improved Semantic Understanding: By leveraging domain knowledge during pre-training, these models develop a deeper understanding of context-specific semantics and relationships between terms unique to that domain. This leads to more precise inference capabilities when generating content like cloze test questions and answers.

Enhanced Performance: Domain-specific PLMs often outperform general-purpose models when tasked with tasks within their specialized area due to their focused training data and fine-tuning processes targeted at optimizing performance metrics relevant to that domain.

Increased Efficiency: Using PLMs designed for specific domains reduces reliance on extensive manual feature engineering or external knowledge bases by inherently capturing intricate nuances present in specialized texts during training stages.

How can human evaluators better distinguish between human-designed and CDGP-generated questions?

Improving human evaluators' ability to differentiate between human-designed questions versus those generated by systems like CDGP involves several strategies:

Diverse Evaluation Criteria: Provide evaluators with clear guidelines encompassing various aspects beyond just correctness—such as question structure coherence, logical flow consistency, vocabulary usage patterns—to discern subtle differences between manually crafted questions vs automated ones accurately.

2Training & Familiarization: Conduct thorough training sessions where evaluators familiarize themselves with common characteristics typical of both types of questions—human-created vs machine-generated—and learn key indicators distinguishing them based on linguistic cues or stylistic variations unique to each category.
3Blind Testing Protocols: Implement blind testing protocols where evaluators assess sets containing mixed samples without prior knowledge about their origin (human vs CDGP). This approach minimizes bias towards either type while encouraging unbiased evaluation solely based on intrinsic qualities.
4Feedback Mechanisms: Establish mechanisms enabling evaluators' feedback post-assessment regarding challenges faced distinguishing question origins; this aids system refinement by identifying areas needing improvement such as mimicking natural language nuances better.
5Continuous Learning: Foster continuous learning opportunities through exposure sessions showcasing diverse examples spanning different subjects/genres created by humans vs machines; this ongoing education enhances evaluator acumen honing skills necessary for accurate differentiation over time.