The SUMMA Platform Prototype

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams

A FRAMEWORK FOR INTELLIGENT VOICE-ENABLED E-EDUCATION SYSTEMS

Although the Internet has received significant attention in recent years, voice is still the most convenient and natural way of communicating between human to human or human to computer. In voice applications, users may have different needs which will require the ability of the system to reason, make decisions, be flexible and adapt to requests during interaction. These needs have placed new requirements in voice application development such as use of advanced models, techniques and methodologies which take into account the needs of different users and environments. The ability of a system to behave close to human reasoning is often mentioned as one of the major requirements for the development of voice applications. In this paper, we present a framework for an intelligent voice-enabled e-Education application and an adaptation of the framework for the development of a prototype Course Registration and Examination (CourseRegExamOnline) module. This study is a preliminary report of an ongoing e-Education project containing the following modules: enrollment, course registration and examination, enquiries/information, messaging/collaboration, e-Learning and library. The CourseRegExamOnline module was developed using VoiceXML for the voice user interface(VUI), PHP for the web user interface (WUI), Apache as the middle-ware and MySQL database as back-end. The system would offer dual access modes using the VUI and WUI. The framework would serve as a reference model for developing voice-based e-Education applications. The e-Education system when fully developed would meet the needs of students who are normal users and those with certain forms of disabilities such as visual impairment, repetitive strain injury (RSI), etc, that make reading and writing difficult

Managing the product development process: a simulation study.

Processes; Simulation; Studies; Product; Product development;

Development of Telephone-based e-Learning Portal

The proliferation of mobile phones in Nigeria, particularly among the student community, has continued to inspire the development and delivery of e-Learning applications. Most of the existing web-based e-Learning applications do not support nomadic voice-based learning (i.e. learning on the move through voice), and consequently do not provide a speedy access to information or enquiries on demand. Internet access is required to get every bit of information from most school portal system, which is not directly available to everyone. Lack of provision for voice in the existing web applications excludes support for people with limited capabilities such as the visually impaired and physical disabilities. In this paper, we present a design and development of a prototype telephone-based e-Learning portal that will be used for course registration and examination. This study is part of an ongoing e-Learning project involving the following modules: enrollment, course registration and examination, enquiries/information, messaging/collaboration, e-Learning and library. The prototype application was developed using VoiceXML for the voice user interface(VUI), PHP for database queries, Apache as the middle-ware and MySQL database as back-end. A unified modelling language (UML) was used to model and design the application. The proposed e-Learning system will compliment the web-based system in other to meet the needs of students with a range of disabilities such as visual impairment, repetitive strain injury, etc, that make reading and writing difficult. It also makes multiple platforms available to all users as well as boosting access to education for the physically challenged, particularly the sight impaired in the developing countries of the world. In institutions where students are not allowed to use mobile phones or where cost is an issue, then the alternative is the use of PC-phone

Development and Deployment of VoiceXML-Based Banking Applications

In recent times, the financial sector has become one of the most vibrant sectors of the Nigerian economy with about twenty five banks after the bank consolidation / merger exercise. This sector presents huge business investments in the area of Information and Communication Technology (ICT). It is also plausible to say that the sector today is the largest body of ICT services and products users. It is no gainsaying the fact that so many Nigerians now carry mobile phones across the different parts of the country. However, applications that provide voice access to real-time banking transactions from anywhere, anytime via telephone are still at their very low stage of adoption across the Nigerian banking and financial sector. A versatile speech-enabled mobile banking application has been developed using VXML, PHP, Apache and MySQL. The developed application provides real-time access to banking services, thus improving corporate bottom-line and Quality of Service (QoS) for customer satisfaction

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is determined by dice rolling. Visit our website http://zhuhao.me/fewre