â  â  â  pragmatic dimension of the contemporary language and principles of curriculum development â â â â â â â  dr. boulaares djemai * â  â  abstract â â â  textbooks have been very significant and valuable for our precedents because education is very significant and through it, everybody can achieve fulfillment. knowledge and its application can be achieved through the person’s interaction...

Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information about the words it have seen before in the sentence, and thus the useful longterm information may be ignored when predicting the next words. In this paper, we propose Attention-based Memory Selection Recurrent Network...

Previous studies in Open Information Extraction (Open IE) are mainly based on extraction patterns. They manually define patterns or automatically learn them from a large corpus. However, these approaches are limited when grasping the context of a sentence, and they fail to capture implicit relations. In this paper, we address this problem with the following methods. First, we exploit long short...

The output of automatic speech recognition systems is generally an unpunctuated stream of words which is hard to process for both humans and machines. We present a two-stage recurrent neural network based model using long short-term memory units to restore punctuation in speech transcripts. In the first stage, textual features are learned on a large text corpus. The second stage combines textua...

Voice Activity Detection (VAD) is critical in speech recognition systems as it can dramatically impact the recognition accuracy especially on noisy data. This paper presents a novel method which applies Minimum Word Error (MWE) training to a Long Short-Term Memory RNN to optimize Voice Activity Detection for speech recognition. Experiments compare speech recognition WERs using RNN VAD with othe...

We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory (LSTM) recurrent neural networks to learn representations of the parser state, we replace lookup based word representations with representations constructed based on the orthograp...

Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning. Current automatic TS techniques are limited to either lexical-level applications or manually defining a large amount of rules. Since deep neural networks are powerful models that have achieved excellent performance over many difficult tasks, in this paper, we prop...