Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models
usually perform poorly when transfer to new domains with limited training samples. Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue. In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation. Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.
Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficult to apply them into practical application in the education field. Thi
s paper for the first time addresses the question-answer pair generation task on the real-world examination data, and proposes a new unified framework on RACE. To capture the important information of the input passage we first automatically generate (rather than extracting) keyphrases, thus this task is reduced to keyphrase-question-answer triplet joint generation. Accordingly, we propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively, and then apply the generated question and keyphrases to guide the generation of answers. To establish a solid benchmark, we build our model on the strong generative pre-training model. Experimental results show that our model makes great breakthroughs in the question-answer pair generation task. Moreover, we make a comprehensive analysis on our model, suggesting new directions for this challenging task.
In recent years, GIS software has undergone great development in many levels, although the basics of GIS have remained constant for a long time. In order to meet the requirements put forward by the global GIS community, Esri™ had to start over with a
n entirely new methodology, laying a solid foundation that would allow performance and experience to guide, the release of ArcGIS Pro. Which is the latest example of this qualitative leap in keeping pace with the visual development of the GIS interface, and the development in methods of sharing and editing on the web. Therefore, the idea of this project came to shed light on this platform, which represents a qualitative leap in the field of GIS software.
The importance of this project comes from the fact that it provides an educational guide with practical applications of the latest GIS software (ArcGIS Pro), where the project objectives can be summarized as follows:
• Provide a step-by-step introductory guide on learning to use the latest ArcGIS™ software ‘ArcGIS Pro’ from Esri ®;
• Topographical and detailed survey of an area within Tishreen University.
• Conducting practical applications within the ArcGIS Pro platform for the study area, by producing a topographical map of the studied area and performing some topographical analyzes.
The project includes four main chapters, the first chapter explains the concepts of geographic information systems (GIS), while the second chapter explains an introductory and detailed guide to the (ArcGIS Pro) platform, and the third and fourth chapters provide a practical application (field and office) to produce a topographic map within the Tishreen University campus, using ArcGIS Pro platform, with a number of spatial analyzes performed.
Leveraging large-scale unlabeled web videos such as instructional videos for pre-training followed by task-specific finetuning has become the de facto approach for many video-and-language tasks. However, these instructional videos are very noisy, the
accompanying ASR narrations are often incomplete, and can be irrelevant to or temporally misaligned with the visual content, limiting the performance of the models trained on such data. To address these issues, we propose an improved video-and-language pre-training method that first adds automatically-extracted dense region captions from the video frames as auxiliary text input, to provide informative visual cues for learning better video and language associations. Second, to alleviate the temporal misalignment issue, our method incorporates an entropy minimization-based constrained attention loss, to encourage the model to automatically focus on the correct caption from a pool of candidate ASR captions. Our overall approach is named DeCEMBERT (Dense Captions and Entropy Minimization). Comprehensive experiments on three video-and-language tasks (text-to-video retrieval, video captioning, and video question answering) across five datasets demonstrate that our approach outperforms previous state-of-the-art methods. Ablation studies on pre-training and downstream tasks show that adding dense captions and constrained attention loss help improve the model performance. Lastly, we also provide attention visualization to show the effect of applying the proposed constrained attention loss.
Children's Museum is one of the most important institutions that
offer exhibits and programs to stimulate educational experiences for
children, through:
-Build a bridge between education and the community to serve the
educational process.
-Linki
ng the museum exhibits and school curricula in order to
enrich the student's piece of information and focus on his mind than
just read in the textbook.
Design children's museums, developed increasingly with the
development of science and technology, and thus the evolution of
potential psychological and mental requirements of the child, and
this is who tried to search him diving for the goal of reaching some
new and useful data in this area.
This paper aims at studying the narrative functions of the stack
Chorus in selected English Renaissance plays: Christopher
Marlowe's Dr. Faustus, Thomas Kyd's The Spanish Tragedy, Ben
Jonson's Volpone. This study offers a close reading of the spee
ches
delivered by the Chorus, and shows their narration of the events and
their moral commentaries. This emphasizes their didactic
significance because they sum up the events, interpret the vague
ones and endorse the play's desired message . this paper concludes
that Jonson develops the stock Chorus by having recourse to other
characters who perform this communicative and didactic role. Here
lies Jonson's contribution to this conventional figure.
This study aimed at investigating the effect of a computerized
programme on changing alternative concepts in science among second
intermediate grade students in the Kingdom of Saudi Arabia in “motion
and sound units”. A puroseful sample of (90) st
udents was selected and
students were randomly assigned to two groups: the experimental group
which was taught “motion and sound” by the computerized programme,
and the control group which was taught the same two units by the
traditional method.
هدفت هذه الدراسة إلى استقصاء فعالية موقع تعليمي على شبكة الإنترنـت لتـدريس
الهندسة في تحصيل و اتجاهات طلبة الصف التاسع في الأردن. و لاختبـار فرضـيات
الدراسة، تم تصميم موقع تعليمي على الإنترنت، كما تم تطـوير اختبـار تحـصيلي
و استبانة تم استخراج دلا
لات صدقهما و ثباتهما. تكونت عينة الدراسة من (60) طالبـاً
و طالبة من طلاب الصف التاسع الأساسي، تم تقسيمهم إلـى مجمـوعتين (مجموعـة
تجريبية و مجموعة ضابطة).
