Distributed Deep Learning for Question Answering

This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification. Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the distributed framework based on the message passing interface can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training. For example, with 48 workers, a 24x speedup is achievable for the answer selection task and running time is decreased from 138.2 hours to 5.81 hours, which will increase the productivity significantly.Comment: This paper will appear in the Proceeding of The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, US

AQUA: an ontology driven question answering system

This paper describes AQUA our question answering over the Web. AQUA was designed to work over heterogeneous sources. This means that AQUA is equipped to work as closed domain and in addition to open-domain question answering. As a first instance, AQUA tries to answer a question using a Knowledge base. If a query cannot be satisfied over a knowledge base/database. Then, AQUA tries to find an answer on web pages (i.e. it uses as corpus the internet as resource). Our system uses NLP (Natural Language Processing), First order logic and Information Extraction technologies. AQUA has been tested using an ontology which describes academic life. Keywords Ontologies, Information Extraction, Machine Learnin

An evaluation of pedagogically informed parameterised questions for self assessment

Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits