Overview Most current applications of automated dialogue systems involve narrowly focused language understanding and simple models of dialogue interaction. Understanding language and generating natural dialogue are important in building friendly interfaces for dialogue system, but it is particularly critical in settings where the speaker is focused on 1D situation. Real human conversation is highly context-dependent, and human speakers jointly build contributions to the shared context. That is, human dialogue has a very complex structure by itself, and exhibits a complex network of relations between other dialogues. AKA has continuously tried to build friendly dialogue interfaces, and understand situation- and context-dependent interpretation of speaker utterances, including multiple situations. Bach, multiple linked dialogue data platform for AKA’s dialogue system, is our solution […]
Overview Bach, multiple linked dialogue data platform for Muse engine, has utilized multiple resources – artificial intelligence, human reviewers, automated rating system, etc. – in an effort to generate best human-machine conversations, and a noisy data follows as a necessity from the development process. Noisy data is meaningless data, and its meaning can be expanded to include abusive language which causes challenges that we encountered when developing Muse engine. In this blog post, we will describe a development process of Muse engine ‘s abusive language detection system and demonstrate the efficacy by comparing the system with different models in detecting abusive language . To be brief, AKA’s abusive language detection system has shown a good performance by extracting additional features […]
Introduction Producing sentences which are perceived as natural by a human is a crucial goal of all automated dialogue systems. It makes interactions more natural, avoids misunderstandings, and leads higher user satisifcation and user trust. However, making high-quality sentences constitutes hard challenges in terms of e.g., improving grammatical accuracy, or using a variety of sentences, or maintaining the context of conversation. Musio has explored a number of approaches to the task of high-quality sentence and is dedicated to making the step towards perfect dialogue system. In this blog post, we introduce the test results to show that Musio has its own peculiar methods to generate sentences and these are pretty well-formed sentences. Grammatical Correctness Musio always makes grammatically correct responses […]
Introduction Dialogue systems are systems intended to converse with human users, and recent advancements in AI have contributed to closing the gap between human-machine conversations in many consumer services. AKA Intelligence researchers also tried to build automated dialogue systems and finally set up its own dialogue system, Muse, being able to practice English in addition to social conversations. One of the key differences between the existing systems and Muse is the customized data structure, Bach, to train AI model. It is important for recent dialogue systems to learn from human-human conversations in order to generate best human-machine conversations. Normally, the process of dialogue system may be summarized as follows: when a user asks a question, the system either searches a […]
Introduction Recent advances in AI has contributed to the rebirth of a chatbot-type dialogue 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. Therefore, it is important to understand the quality attributes associated developing and implementing high-quality conversational agents and diaglouge system. Muse is a NLP engine developed by AKA Intelligence, with a focus on natural conversation. Engineers at AKA and Softbank are collaborating to bring the Muse engine into Pepper, Softbank’s humannoid robot, to use Muse as Pepper’s English conversation system. Muse is also expanding into other hardware platforms as well. A typical example is Musio, a […]
Today, we have the sneak preview of MUSE API (Beta). MUSE API is what Musio actually talks to in the cloud to manage the dialogue and generate things to say, recognize faces, etc. In order to show you better some of the major things going on behind the Musio, we built a temporary front-end for the API. We have a video explaining features of API as well as accompanying material. Our API is on its way, so stay tuned!
Table of Contents 1. Conditional Neural Network Architectures 1.1. goal 1.2. motivation 1.3. ingredients 1.4. steps 1.5. outlook 1.6. resources Conditional Neural Network Architectures goal Today we are going to have a look at conditional neural network architectures and present some of the findings in the recent papers “Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer” and “PathNet: Evolution Channels Gradient Descent in Super Neural Networks”. motivation The interest in conditional models is mainly based on their capability to incorporate a huge number of parameters compared to standard architectures without increasing the need for more computationally powerful hardware. Furthermore such models seem to be able to reduce the training time and are interesting for multi-, online-task learning and transfer learning. […]