scholarly journals Generating Distractors for Reading Comprehension Questions from Real Examinations

2019 ◽  
Vol 33 ◽  
pp. 6423-6430 ◽  
Author(s):  
Yifan Gao ◽  
Lidong Bing ◽  
Piji Li ◽  
Irwin King ◽  
Michael R. Lyu
Keyword(s):  
Reading Comprehension ◽  
Multiple Choice ◽  
Semantic Context ◽  
Time Step ◽  
Word Level ◽  
Human Evaluation ◽  
Sentence Level ◽  
Dynamic Attention

We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor to generate longer and semantic-rich distractors which are closer to distractors in real reading comprehension from examinations. Taking a reading comprehension article, a pair of question and its correct option as input, our goal is to generate several distractors which are somehow related to the answer, consistent with the semantic context of the question and have some trace in the article. We propose a hierarchical encoderdecoder framework with static and dynamic attention mechanisms to tackle this task. Specifically, the dynamic attention can combine sentence-level and word-level attention varying at each recurrent time step to generate a more readable sequence. The static attention is to modulate the dynamic attention not to focus on question irrelevant sentences or sentences which contribute to the correct option. Our proposed framework outperforms several strong baselines on the first prepared distractor generation dataset of real reading comprehension questions. For human evaluation, compared with those distractors generated by baselines, our generated distractors are more functional to confuse the annotators.

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 Using Sentence-Level Neural Network Models for Multiple-Choice Reading Comprehension Tasks

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yuanlong Wang ◽  
Ru Li ◽  
Hu Zhang ◽  
Hongyan Tan ◽  
Qinghua Chai
Keyword(s):  
Neural Network ◽  
Reading Comprehension ◽  
Sentence Comprehension ◽  
Multiple Choice ◽  
Network Models ◽  
Choice Task ◽  
Chinese Reading ◽  
Neural Network Models ◽  
Attention Model ◽  
Sentence Level

Comprehending unstructured text is a challenging task for machines because it involves understanding texts and answering questions. In this paper, we study the multiple-choice task for reading comprehension based on MC Test datasets and Chinese reading comprehension datasets, among which Chinese reading comprehension datasets which are built by ourselves. Observing the above-mentioned training sets, we find that “sentence comprehension” is more important than “word comprehension” in multiple-choice task, and therefore we propose sentence-level neural network models. Our model firstly uses LSTM network and a composition model to learn compositional vector representation for sentences and then trains a sentence-level attention model for obtaining the sentence-level attention between the sentence embedding in documents and the optional sentences embedding by dot product. Finally, a consensus attention is gained by merging individual attention with the merging function. Experimental results show that our model outperforms various state-of-the-art baselines significantly for both the multiple-choice reading comprehension datasets.

Extending the efficacy of Collaborative Strategic Reading (CSR) in teaching reading strategies: A socio-cultural emotional perspective

2021 ◽  
pp. 136216882110115
Author(s):  
Ali Amjadi ◽  
Seyed Hassan Talebi
Keyword(s):  
Reading Comprehension ◽  
Rural Areas ◽  
Reading Strategies ◽  
Multiple Choice ◽  
Strategy Instruction ◽  
Teaching Reading ◽  
Social Emotional ◽  
Reading Tests ◽  
Strategic Reading ◽  
Collaborative Strategic Reading

Implementing social-emotional learning skills into Collaborative Strategic Reading (CSR), the current study intended to extend the efficacy of CSR for teaching reading strategies when applying it to students in rural areas from a working-class community. To this purpose, forty-four students who made the comparison and the experimental groups were taught reading strategies through CSR and ECSR (Extended Collaborative Strategic Reading), respectively. A reading comprehension test with different question types was implemented to the students as pretest and posttest, and an interview was given at the end of the study to investigate the perception of the students toward reading strategy instruction through CSR and ECSR. Analysis of data indicated that only the ECSR group improved significantly in overall reading comprehension, but the componential analysis of the reading test showed that despite the fact that the CSR group showed no significant improvement in the reading tests in four formats (true–false, multiple-choice, matching, and cloze), the ECSR group improved significantly in reading tests with multiple-choice and cloze test formats. Moreover, although the students in both groups showed a positive view toward the interventions, the students in the ECSR group improved in social-emotional and communication skills. It seems that CSR can be improved to be effective by implementing the emotional component to it.

Deaf and Hard-of-hearing Users Evaluating Designs for Highlighting Key Words in Educational Lecture Videos

2021 ◽  
Vol 14 (4) ◽  
pp. 1-24
Author(s):  
Sushant Kafle ◽  
Becca Dingman ◽  
Matt Huenerfauth
Keyword(s):  
Experimental Study ◽  
Key Words ◽  
Hard Of Hearing ◽  
Experimental Studies ◽  
Design Parameters ◽  
Educational Video ◽  
Word Level ◽  
Sentence Level ◽  
First Occurrence ◽  
Educational Videos

There are style guidelines for authors who highlight important words in static text, e.g., bolded words in student textbooks, yet little research has investigated highlighting in dynamic texts, e.g., captions during educational videos for Deaf or Hard of Hearing (DHH) users. In our experimental study, DHH participants subjectively compared design parameters for caption highlighting, including: decoration (underlining vs. italicizing vs. boldfacing), granularity (sentence level vs. word level), and whether to highlight only the first occurrence of a repeating keyword. In partial contrast to recommendations in prior research, which had not been based on experimental studies with DHH users, we found that DHH participants preferred boldface, word-level highlighting in captions. Our empirical results provide guidance for the design of keyword highlighting during captioned videos for DHH users, especially in educational video genres.

scholarly journals Generating Sentences by Editing Prototypes

2018 ◽  
Vol 6 ◽  
pp. 437-450 ◽  
Author(s):  
Kelvin Guu ◽  
Tatsunori B. Hashimoto ◽  
Yonatan Oren ◽  
Percy Liang
Keyword(s):  
Language Model ◽  
Language Modeling ◽  
Language Models ◽  
Training Corpus ◽  
Human Evaluation ◽  
Sentence Level ◽  
Sentence Similarity ◽  
Traditional Language ◽  
Generative Language

We propose a new generative language model for sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional language models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies.

scholarly journals Understanding Syntactic and Semantic Errors in the Composition Writing of Jordanian EFL Learners

2017 ◽  
Vol 6 (6) ◽  
pp. 158
Author(s):  
Yazan Shaker Almahameed ◽  
May Al-Shaikhli
Keyword(s):  
Foreign Language ◽  
Language Learners ◽  
Semantic Knowledge ◽  
Resumptive Pronouns ◽  
Foreign Language Learners ◽  
Word Level ◽  
Sentence Level ◽  
Native Speakers Of English ◽  
Semantic Errors ◽  
Verb Tense

The current study aimed at investigating the salient syntactic and semantic errors made by Jordanian English foreign language learners as writing in English. Writing poses a great challenge for both native and non-native speakers of English, since writing involves employing most language sub-systems such as grammar, vocabulary, spelling and punctuation. A total of 30 Jordanian English foreign language learners participated in the study. The participants were instructed to write a composition of no more than one hundred and fifty words on a selected topic. Essays were collected and analyzed statistically to obtain the needed results. The results of the study displayed that syntactic errors produced by the participants were varied, in that eleven types of syntactic errors were committed as follows; verb-tense, agreement, auxiliary, conjunctions, word order, resumptive pronouns, null-subject, double-subject, superlative, comparative and possessive pronouns. Amongst syntactic errors, verb tense errors were the most frequent with 33%. The results additionally revealed that two types of semantic errors were made; errors at sentence level and errors at word level. Errors at word level outstripped by far errors at sentence level, scoring respectively 82% and 18%. It can be concluded that the syntactic and semantic knowledge of Jordanian learners of English is still insufficient.

scholarly journals Product reputation mining: bring informative review summaries to producers and consumers

2019 ◽  
Vol 16 (2) ◽  
pp. 359-380
Author(s):  
Zhehua Piao ◽  
Sang-Min Park ◽  
Byung-Won On ◽  
Gyu Choi ◽  
Myong-Soon Park
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Word Level ◽  
Sentence Level ◽  
Survey Results ◽  
Statistical Studies ◽  
Sentiment Orientation ◽  
Level Method ◽  
Points Of View ◽  
Graph Based Model

Product reputation mining systems can help customers make their buying decision about a product of interest. In addition, it will be helpful to investigate the preferences of recently released products made by enterprises. Unlike the conventional manual survey, it will give us quick survey results on a low cost budget. In this article, we propose a novel product reputation mining approach based on three dimensional points of view that are word, sentence, and aspect?levels. Given a target product, the aspect?level method assigns the sentences of a review document to the desired aspects. The sentence?level method is a graph-based model for quantifying the importance of sentences. The word?level method computes both importance and sentiment orientation of words. Aggregating these scores, the proposed approach measures the reputation tendency and preferred intensity and selects top-k informative review documents about the product. To validate the proposed method, we experimented with review documents relevant with K5 in Kia motors. Our experimental results show that our method is more helpful than the existing lexicon?based approach in the empirical and statistical studies.

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