IBM Research India - Conversational AI

Conversational AI

As we move towards an increasingly enriched digital world, conversational Artificial Intelligence or Conversational AI I is being used to enable communication with that world. A dominant current example of this technology is chatbots or virtual assistants. 
Over the last few years there has been a tremendous growth in the deep learning based conversation modeling techniques based on sequence to sequence modeling, transformer architectures and pre-trained large language models. Despite of these great advancements. these models are still far from being useful for their deployments in industrial settings and most of the current deployed chatbots use bot building frameworks such as Watson Assistant, DialogFlow and lex. These frameworks provide easy ways for modeling user intents using machine learning methods and use a rule based dialog flow. Building chatbots using these frameworks could take a long time as they may require a lot of human involvement. 
The deep learning based frameworks are completely data driven and do not require involvement from domain experts. On the other hand, bot building frameworks require a lot of human involvement. Despite the huge cost and time involved, enterprises use bot building frameworks as they provide good control for what the end users experience. 
At IBM research India, we are working on various approaches that could help bring the deep learning based models closer to their use in enterprise settings. 
Grounded Response Generation
One of the key problem that deep learning based systems based on pre-trained large language models suffer is the problem of hallucination. They could provide factually incorrect or inconsistent information. To overcome this problem, we are exploring ways in which we ground the dialog response generation on trustworthy enterprise content. We are exploring methods in which we could use approved unstructured documents while response generation. We are also exploring ways in which we could use decision trees or flow charts as well as structured data for grounding dialog responses.
Human in the Loop Approach
Bootstrapping and continuous improvement of chatbots

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