Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations

Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from schema.org annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.Comment: Presented in the First International Workshop on Chatbots co-located with ICWSM 2018 in Stanford, C

Dialog-based Language Learning

A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of (Weston et al., 2015) and large-scale question answering from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all

Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user's responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.Comment: ACL 201

Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases

Goal-oriented dialog agents are expected to recognize user-intentions from an utterance and execute appropriate tasks. Typically, such systems use a semantic parser to solve this problem. However, semantic parsers could fail if user utterances contain out-of-grammar words/phrases or if the semantics of uttered phrases did not match the parser’s expectations. In this work, we have explored a more robust method of task prediction. We define task prediction as a classification problem, rather than “parsing ” and use semantic contexts to improve classification accuracy. Our classifier uses semantic smoothing kernels that can encode information from knowledge bases such as Wordnet, NELL and Freebase.com. Our experiments on two spoken language corpora show that augmenting semantic information from these knowledge bases gives about 30 % absolute improvement in task prediction over a parserbased method. Our approach thus helps make a dialog agent more robust to user input and helps reduce number of turns required to detected intended tasks.