scholarly journals DCMN+: Dual Co-Matching Network for Multi-Choice Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 9563-9570 ◽  
Author(s):  
Shuailiang Zhang ◽  
Hai Zhao ◽  
Yuwei Wu ◽  
Zhuosheng Zhang ◽  
Xi Zhou ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Reading Strategies ◽  
State Of The Art ◽  
Matching Network ◽  
Question And Answer ◽  
The Relationship ◽  
Answer Option ◽  
Comparison Information

Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question. Previous approaches usually only calculate question-aware passage representation and ignore passage-aware question representation when modeling the relationship between passage and question, which cannot effectively capture the relationship between passage and question. In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. Besides, inspired by how humans solve multi-choice questions, we integrate two reading strategies into our model: (i) passage sentence selection that finds the most salient supporting sentences to answer the question, (ii) answer option interaction that encodes the comparison information between answer options. DCMN equipped with the two strategies (DCMN+) obtains state-of-the-art results on five multi-choice reading comprehension datasets from different domains: RACE, SemEval-2018 Task 11, ROCStories, COIN, MCTest.

Self-Efficacy Sources, Reading Self-Efficacy, Metacognitive Reading Strategies and Reading Comprehension Performance: A Correlational Study in Saudi EFL Context

2021 ◽  
Vol 7 (2) ◽  
pp. 19-35
Author(s):  
Muhammad Waleed Shehzad ◽  
Ishtiaq Hussain ◽  
Amer Akhtar ◽  
Saadia Fatima
Keyword(s):  
Reading Comprehension ◽  
Self Efficacy ◽  
Reading Strategies ◽  
Structural Equation ◽  
Physiological State ◽  
Positive Association ◽  
Efficacy Beliefs ◽  
Correlational Design ◽  
Metacognitive Reading Strategies ◽  
The Relationship

Abstract The intended aim of this research was to identify the connection of Self-Efficacy Sources (SES) and Metacognitive Reading Strategies (MCRS) with Reading Comprehension (RC) by deploying reading Self-Efficacy Beliefs (SEB) as a mediating construct. A correlational design was utilized. Proportionate stratified random sampling was deployed to select a sample of 383 Saudi EFL university learners. Questionnaires and a reading comprehension test were employed to gather the data. Structural equation modelling was used to test the relationships. Results indicated that SES were substantially associated with SEB except physiological state. Moreover, all the three MCRS showed significant and positive association with SEB. Also, SEB were substantially associated with RC. Regarding mediation, it was discovered that SEB mediated the relationship among SES and RC except one source, i.e., physiological state. Moreover, SEB mediated the association between all the three MCRS and RC. This study provides several implications for learners, teachers, and policymakers. Keywords: Metacognitive Reading Strategies, Self-efficacy Sources, Reading Self-efficacy Beliefs, Reading Comprehension, Saudi EFL Learners

scholarly journals Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 9725-9732
Author(s):  
Xiaorui Zhou ◽  
Senlin Luo ◽  
Yunfang Wu
Keyword(s):  
Reading Comprehension ◽  
Semantic Similarity ◽  
State Of The Art ◽  
Deep Understanding ◽  
Hierarchical Architecture ◽  
Hierarchical Network ◽  
Word Level ◽  
Human Evaluation ◽  
Sentence Level ◽  
The Relationship

In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-sequence (Seq2Seq) model show great potential in generating creative text, the previous neural methods for distractor generation ignore two important aspects. First, they didn't model the interactions between the article and question, making the generated distractors tend to be too general or not relevant to question context. Second, they didn't emphasize the relationship between the distractor and article, making the generated distractors not semantically relevant to the article and thus fail to form a set of meaningful options. To solve the first problem, we propose a co-attention enhanced hierarchical architecture to better capture the interactions between the article and question, thus guide the decoder to generate more coherent distractors. To alleviate the second problem, we add an additional semantic similarity loss to push the generated distractors more relevant to the article. Experimental results show that our model outperforms several strong baselines on automatic metrics, achieving state-of-the-art performance. Further human evaluation indicates that our generated distractors are more coherent and more educative compared with those distractors generated by baselines.

scholarly journals The Relationship between Learning Style Preferences and Reading Strategy Use in English as a Foreign Language

2018 ◽  
Vol 34 ◽  
pp. 93-103
Author(s):  
Elżbieta Danuta Lesiak-Bielawska
Keyword(s):  
Reading Comprehension ◽  
Foreign Language ◽  
Learning Style ◽  
Reading Strategies ◽  
Reading Strategy ◽  
Strategy Use ◽  
Learning Style Preferences ◽  
Task Requirements ◽  
Style Preferences ◽  
The Relationship

The study explored the relationship between learning style preferences and the use of reading strategies triggered during the performance of a reading comprehension assignment in English as a foreign language (EFL). The research conducted drew on the hypothesis that the type of language task activates a battery of strategies that reflect the subject's learning style preferences and the task requirements.

scholarly journals Multi-Matching Network for Multiple Choice Reading Comprehension

2019 ◽  
Vol 33 ◽  
pp. 7088-7095 ◽  
Author(s):  
Min Tang ◽  
Jiaran Cai ◽  
Hankz Hankui Zhuo
Keyword(s):  
Reading Comprehension ◽  
Correct Answer ◽  
Large Scale ◽  
State Of The Art ◽  
Multiple Choice ◽  
Semantic Relationship ◽  
Matching Network ◽  
Empirical Results ◽  
Proposed Model ◽  
Machine Reading

Multiple-choice machine reading comprehension is an important and challenging task where the machine is required to select the correct answer from a set of candidate answers given passage and question. Existing approaches either match extracted evidence with candidate answers shallowly or model passage, question and candidate answers with a single paradigm of matching. In this paper, we propose Multi-Matching Network (MMN) which models the semantic relationship among passage, question and candidate answers from multiple different paradigms of matching. In our MMN model, each paradigm is inspired by how human think and designed under a unified compose-match framework. To demonstrate the effectiveness of our model, we evaluate MMN on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Empirical results show that our proposed model achieves a significant improvement compared to strong baselines and obtains state-of-the-art results.

The Relationship Between Gender And Reading Comprehension At College Level

2017 ◽  
pp. 426
Author(s):  
Ala' Hussain Oda ◽  
Mohsen R. Abdul-Kadhim
Keyword(s):  
Reading Comprehension ◽  
College Level ◽  
The Relationship

Adolescent Language: Models, Assessment, and Links to Reading

2019 ◽  
Author(s):  
Amanda Goodwin ◽  
Yaacov Petscher ◽  
Jamie Tock
Keyword(s):  
Reading Comprehension ◽  
Bifactor Model ◽  
Language Models ◽  
Multiple Group ◽  
Global Factor ◽  
Eighth Grade Students ◽  
Key Aspects ◽  
Future Work ◽  
The Relationship ◽  
Best Fit

Various models have highlighted the complexity of language. Building on foundational ideas regarding three key aspects of language, our study contributes to the literature by 1) exploring broader conceptions of morphology, vocabulary, and syntax, 2) operationalizing this theoretical model into a gamified, standardized, computer-adaptive assessment of language for fifth to eighth grade students entitled Monster, PI, and 3) uncovering further evidence regarding the relationship between language and standardized reading comprehension via this assessment. Multiple-group item response theory (IRT) across grades show that morphology was best fit by a bifactor model of task specific factors along with a global factor related to each skill. Vocabulary was best fit by a bifactor model that identifies performance overall and on specific words. Syntax, though, was best fit by a unidimensional model. Next, Monster, PI produced reliable scores suggesting language can be assessed efficiently and precisely for students via this model. Lastly, performance on Monster, PI explained more than 50% of variance in standardized reading, suggesting operationalizing language via Monster, PI can provide meaningful understandings of the relationship between language and reading comprehension. Specifically, considering just a subset of a construct, like identification of units of meaning, explained significantly less variance in reading comprehension. This highlights the importance of considering these broader constructs. Implications indicate that future work should consider a model of language where component areas are considered broadly and contributions to reading comprehension are explored via general performance on components as well as skill level performance.

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