Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent

Yun-Hsuan Jen, Chieh-Yang Huang, MeiHua Chen, Ting-Hao Huang, Lun-Wei Ku


Abstract
Many English-as-a-second language learners have trouble using near-synonym words (e.g., small vs.little; briefly vs.shortly) correctly, and often look for example sentences to learn how two nearly synonymous terms differ. Prior work uses hand-crafted scores to recommend sentences but has difficulty in adopting such scores to all the near-synonyms as near-synonyms differ in various ways. We notice that the helpfulness of the learning material would reflect on the learners’ performance. Thus, we propose the inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent’s performance. To enable the agent to behave like a learner, we leverage entailment modeling’s capability of inferring answers from the provided materials. Experimental results show that the proposed agent is equipped with good learner-like behavior to achieve the best performance in both fill-in-the-blank (FITB) and good example sentence selection tasks. We further conduct a classroom user study with college ESL learners. The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently. Compared to other models, the proposed agent improves the score of more than 17% of students after learning.
Anthology ID:
2020.emnlp-main.312
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3807–3817
Language:
URL:
https://aclanthology.org/2020.emnlp-main.312
DOI:
10.18653/v1/2020.emnlp-main.312
Bibkey:
Cite (ACL):
Yun-Hsuan Jen, Chieh-Yang Huang, MeiHua Chen, Ting-Hao Huang, and Lun-Wei Ku. 2020. Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3807–3817, Online. Association for Computational Linguistics.
Cite (Informal):
Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent (Jen et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.312.pdf
Optional supplementary material:
 2020.emnlp-main.312.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38939314
Code
 joyyyjen/Inference-Based-Learner-Like-Agent