THE IMPLICATION ON TEACHING EFL (ENGLISH FOREIGN LANGUAGE) READING FUN TO VARIOUS LEVELS OF INDONESIAN STUDENTS

For some students, EFL (English Foreign Language) reading is one of the most uninteresting subjects because it can make them bored easily. Kweldju (1996) found that students were not interested in reading although they thought some texts books were useful for their study. Meanwhile, English is a compulsory subject that must be studied by Indonesian students. If they get bad to comprehend EFL reading, it means that their English is still bad so that they cannot get the information from the reading text. The goal of the teaching EFL reading in Indonesia is to comprehend the reading texts on which Indonesian students must be able to read science-related texts written in English. This paper analyzes some strategies such as metacognitive and extensive reading to make students enjoyable when learning English reading

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues

THE IMPLICATION OF FUNCTIONAL THEORY IN TEACHING READING A DESCRIPTIVE TEXT FOR MIDDLE AGE STUDENTS (Functional Communication Activities in Language Teaching)

Functional theory views language as means of communication. So, communicative competence is the goal of language teaching. One of the most characteristic features of communicative language teaching is that it pays systemic attention to functional as well as structural aspects of language, combining these into a more fully communicative view. Teaching language as communication focuses on the ability to use language for different purposes. In this article the writer focused on functional communication activities in language teaching. The aim of this article is to know the implementation of functional communication activities in teaching reading a descriptive text for middle age students