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

modularity of reading comprehension in multiple-choice cloze procedure and reliability fluctuation

هدف این رساله تحقیق در مورد یافتن ارتباط بین واحدهای درک مطلب (از قبیل پروسه واژگانی، پروسه ساختاری و پروسه تفسیر معنایی) از طریق تست کلوز چهار چوابی در بین دانشجویان سال آخر رشته زبان انگلیسی میباشد. در مرحله اول از 150 دانشجو (80 مونث و 70 مذکر) از بین دانشجویان کارشناسی و کارشناسی ارشد دانشگاههای علامه طباطبائی، دانشگاه شهید بهشتی و دانشگاه آزاد اسلامی واحد شمال تست نلسون (450 دی) گرفته شد. ...

Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension

Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole, in this paper, we propose to use a neural network ‘skill’ transfer approach. We transfer knowledge from several lower-level language tasks (skills) including textual entailment, named entity recognition, paraphrase d...

Location Of The Topic Sentence, Level Of Language Proficiency, and Reading Comprehension

Modelling Reading Times in Bilingual Sentence Comprehension

Relatively little is known about the interaction between a bilingual’s two languages beyond the word level. This paper investigates the issue by comparing word reading times (RTs) in both L1 and L2 to quantitative predictions by statistical language models. Recurrent neural networks are trained on either a Dutch corpus, an English corpus, or the two corpora combined (i.e., the bilingual network...

Reading Comprehension using Entity-based Memory Network

This paper introduces a novel neural network model for question answering, the entity-based memory network. It enhances neural networks’ ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities’ states. These entities’ states are continuously updated according to the inpu...