Collaborating Authors

Generalized Zero-shot Intent Detection via Commonsense Knowledge Artificial Intelligence

Identifying user intents from natural language utterances is a crucial step in conversational systems that has been extensively studied as a supervised classification problem. However, in practice, new intents emerge after deploying an intent detection model. Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents -- unseen intents emerge after deployment and they do not have training data. The few existing models that target this setting rely heavily on the scarcely available training data and overfit to seen intents data, resulting in a bias to misclassify utterances with unseen intents into seen ones. We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels; these features are computed by considering how the concepts in an utterance are linked to those in an intent label via commonsense knowledge. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly increase the accuracy of detecting both seen and unseen intents and that RIDE outperforms the state-of-the-art model for unseen intents.

10 Steps to Train an Effective Chatbot and its Machine Learning Models


With the majority of consumers spending significant time on various messaging platforms, brands are turning to these messaging platforms to better interact with consumers. The increase in private messaging between customers and brands is driving companies to turn to chatbots for improved social customer care. The Watson Conversation Service offers a simple, scalable and science-driven solution for developers to build powerful chat bots to address the needs of various brands and companies. As developers leverage Watson Conversation to build cognitive solutions for various, one recurring question is: "How much time should I plan to train my solution" or "How do I know when my model is trained sufficiently well"? While the answer depends greatly on the problem being solved and the data powering the solution, in this blog we offer a common methodology for training the machine learning (ML) models powering your chat bot solution.

Zero-shot User Intent Detection via Capsule Neural Networks Artificial Intelligence

User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users' utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: INTENT-CAPSNET that extracts semantic features from utterances and aggregates them to discriminate existing intents, and INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.

Implementing a Natural Language Classifier in iOS with Keras Core ML


IBM Watson NLC and Conversation services (as well as many other NLU cloud platforms) provide a Swift SDK to use in custom apps to implement intent understanding from natural language utterances. These SDKs and the corresponding NLU platforms are super powerful. They provide much more than simply intent understanding capability -- they also detect entities/slots and provide tools to manage complex, long running conversation dialogs. However, even for the most basic NLC inference, these SDKs depend on network connectivity, as the NLC model is run in the Cloud. By using Core ML models to run NLC and NLU algorithms on the device, we can provide similar functionality without relying on cloud inference.

Chatbots, Designed Paths & Desired Paths


Conversational designers have training and expertise in crafting engaging conversations. Generally conversations are crafted around products and services. Hence a big part of the process is to improve the conversations by focusing & improving the design. For conversational interfaces, a big cause of missed intents from user utterances are new product and new services. The problem here is customers want to chat to your chatbot based on advertising and marketing, but the intents have not been updated.