  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.
