The Generation of Textual Entailment with NLML in an Intelligent Dialogue system for Language Learning CSIEC

arXiv.org Artificial Intelligence

This research report introduces the generation of textual entailment within the project CSIEC (Computer Simulation in Educational Communication), an interactive web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project. Up to now we have found few literatures related with GTE. Simulating the process that a human being learns English as a foreign language we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition (matching), and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.


An AI Framework to Teach English as a Foreign Language: CSIEC

AI Magazine

CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions—including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic—can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of our dialogue system, as well as a survey of related works, we will illustrate the system structure, and describe its pedagogical functions with the underlying AI techniques in detail such as NLP and rule-based reasoning. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process. Finally, we will conclude with potential improvements.


This Amazon Echo mod lets Alexa understand sign language

#artificialintelligence

It seems like voice interfaces are going to be a big part of the future of computing; popping up in phones, smart speakers, and even household appliances. But how useful is this technology for people who don't communicate using speech? Are we creating a system that locks out certain users? These were the questions that inspired software developer Abhishek Singh to create a mod that lets Amazon's Alexa assistant understand some simple sign language commands. In a video, Singh demonstrates how the system works.


Text Box Size, Skill, and Iterative Practice in a Writing Task

AAAI Conferences

Although freewriting strategies are commonly taught in composition courses, there have been few empirical studies on freewriting. We address this gap by examining effects of prior writing skills (as measured by a pre-write essay), freewriting training, text-box size (1, 10, 20 lines), and repetitive writing on freewriting quality. Participants watched an agent-based vicarious learning freewriting instruction video or a control video including brief instructions on freewriting. After training, participants wrote six freewrites, two in each box size. Lesson delivery and text box size did not affect expert human ratings of the freewrites. Furthermore, participants did not benefit from writing successive freewrites regardless of their initial skill level. We describe how these results have been used to inform the design of Writing-Pal, an essay-writing intelligent tutoring system.


Machines Are Developing Language Skills Inside Virtual Worlds

MIT Technology Review

Machines are learning to process simple commands by exploring 3-D virtual worlds. Devices like Amazon's Alexa and Google Home have brought voice-controlled technology into the mainstream, but these still only deal with simple commands. Making machines smart enough to handle a real conversation remains a very tough challenge. And it may be difficult to achieve without some grounding in the way the physical world works. Attempts to solve this problem by hard-coding relationships between words and objects and actions requires endless rules, making a machine unable to adapt to new situations.