Facebook Research Understanding Dialog

Pointing towards Facebooks interest in AI and chatbots.  Dialog understanding is hard.

The long game towards understanding dialog
By: Alexandre Lebrun, Antoine Bordes, Leon Bottou, Marco Baroni

Building an effective dialog system
At Facebook AI Research (FAIR), understanding dialogue is an ambitious and long-term AI research goal.

A truly effective dialogue system will be an assistive technology that will likely include a chatbot-type of system being able to interact with people through natural language communication. This could help people better understand the world around them and communicate more effectively with others, effectively bridging communication gaps. Researching and developing these kinds of technologies will only become more important as the amount of digital content continues to grow.

The attempt to understand and interpret dialogue is not a new one. As far back as 20 years, there were several efforts to build a machine a person could talk to and teach how to have a conversation. These incorporated technology and engineering, but were single purposed with a very narrow focus, using pre-programmed scripted responses.

Thanks to progress in machine learning, particularly in the last few years, having AI agents being able to converse with people in natural language has become a more realistic endeavor that is garnering attention from both the research community and industry.

However, most of today’s dialogue systems continue to be scripted: their natural language understanding module may be based on machine learning, but what they execute or answer is in general dictated by if/then statements or rules engines. While they are improvement on what existed decades ago, it is in large part due to the large databases of content used to create and script their responses.  .... " 

Facebook Research.

Facebook Platform for Dialog Research

Brought to my attention.   Facebook is doing lots with dialog and hybrid interactions, so its a natural place for this kind of research.  Essential for chatbots.  Have yet to see their Facebook M.   Note the use of Mechanical Turk for examples.

ParlAI: A new software platform for dialog research
Jason Weston  Alexander Miller  Will Feng

One of the long-term goals in AI is to develop intelligent chat bots that can converse with people in a natural way. Existing chat bots can sometimes complete specific independent tasks but have trouble understanding more than a single sentence or chaining subtasks together to complete a bigger task. More complex dialog, such as booking a restaurant or chatting about sports or news, requires the ability to understand multiple sentences and then reason about those sentences to supply the next part of the conversation. Since human dialog is so varied, chat bots must be skilled at many related tasks that all require different expertise but use the same input and output format. To achieve these goals, it is necessary to build software that unifies these tasks, as well as the agents that can learn from them.

Recognizing this need, the Facebook AI Research (FAIR) team has built a new, open source platform for training and testing dialog models across multiple tasks at once. ParlAI (pronounced “par-lay”) is a one-stop shop for dialog research, where researchers can submit new tasks and training algorithms to a single, shared repository. Integration with Mechanical Turk for data collection, training, and evaluation also allows bots in ParlAI to talk to humans. This works toward the goal of unifying existing dialog data sets with learning systems that contain real dialog between people and bots.

ParlAI complements existing FAIR text research efforts like FastText, our quick and efficient text classification tool, and CommAI, our framework for developing artificial general intelligence through increasingly complex tasks. .... " 

Further: Facebook Research blog