SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners

@article{Liu2022SCQuesAS,
  title={SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners},
  author={Qiongqiong Liu and Shuyan Huang and Zitao Liu and Weiqing Luo},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.12036}
}
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely used for students learning English as a Second Language (ESL). In this paper, we present a large-scale SC dataset, SC-Ques, which is made up of 292,517 ESL SC questions from real-world standardized English examinations. Furthermore, we build a comprehensive benchmark of automatically solving the SC questions by… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 51 REFERENCES

Assessment of Word-Level Neural Language Models for Sentence Completion

A bidirectional version of RNN LM is presented, which surpassed the previous best results on Microsoft Research Sentence Completion Challenge and the Scholastic Aptitude Test (SAT) sentence completion questions and established state-of-the-art results on the MSR and SAT sets.

Computational Approaches to Sentence Completion

It is found that by fusing local and global information, the ability of algorithms to distinguish sense from nonsense based on a variety of sentence-level phenomena can exceed 50% on this task (chance baseline is 20%), and some avenues for further research are suggested.

Assessing the Effectiveness of Corpus-Based Methods in Solving SAT Sentence Completion Questions

  • E. Tang
  • Computer Science
    J. Comput.
  • 2016
The results of this study demonstrate that local context is a fairly strong measure in determining how well a word fits in a sentence and that exploration of non-similarity based methods may be required to further increase the ability of computers to answer such questions.

CMU Multiple-choice Question Answering System at NTCIR-11 QA-Lab

CMU’s UIMA-based modular automatic question answering (QA) system answers multiplechoice English questions for the world history entrance exam and generates veriable assertions for each answer choice.

The Microsoft Research Sentence Completion Challenge

This work presents the MSR Sentence Completion Challenge Data, which consists of 1,040 sentences, each of which has four impostor sentences, in which a single (fixed) word in the original sentence has been replaced by an imposter word with similar occurrence statistics.

Multiple Choice Question (MCQ) Answering System for Entrance Examination

The article presents the experiments carried out as part of the participation in the pilot task of QA4MRE@CLEF 2013. In the developed system, we have first generated answer pattern by combining the

Sentence completion task using web-scale data

  • Kyusong LeeG. G. Lee
  • Computer Science
    2014 International Conference on Big Data and Smart Computing (BIGCOMP)
  • 2014
The accuracy of the proposed method to automatically answer SAT-style sentence completion questions using web-scale data improved by 52-87% over the current state-of-the-art.

SciTaiL: A Textual Entailment Dataset from Science Question Answering

A new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem, is presented, and it is demonstrated that one can improve accuracy on SCITAIL by 5% using a new neural model that exploits linguistic structure.

I Know What You Want to Express: Sentence Element Inference by Incorporating External Knowledge Base

This work treats an SVO sentence as a three-element triple (subject, sentence pattern, object), and cast the sentence object completion problem as an element inference problem in the proposed TRANSFER model, which leverages the external knowledge base to strengthen the representation learning performance.

Enabling Language Models to Fill in the Blanks

It is shown that humans have difficulty identifying sentences infilled by the approach, which can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics.
...