SummaryHumans develop their concept of an object by classifying it into a category, and acquire language by interacting with others at the same time. Thus, the meaning of a word can be learnt by connecting the recognized word and concept. We consider such an ability to be important in allowing robots to flexibly develop their knowledge of language and concepts. Accordingly, we propose a method that enables robots to acquire such knowledge. The object concept is formed by classifying multimodal information acquired from objects, and the language model is acquired from human speech describing object features. We propose a stochastic model of language and concepts, and knowledge is learnt by estimating the model parameters. The important point is that language and concepts are interdependent. There is a high probability that the same words will be uttered to objects in the same category. Similarly, objects to which the same words are uttered are highly likely to have the same features. Using this relation, the accuracy of both speech recognition and object classification can be improved by the proposed method. However, it is difficult to directly estimate the parameters of the proposed model, because there are many parameters that are required. Therefore, we approximate the proposed model, and estimate its parameters using a nested PitmanYor language model and multimodal latent Dirichlet allocation to acquire the language and concept, respectively. The experimental results show that the accuracy of speech recognition and object classification is improved by the proposed method.
This paper presents a taxonomy of errors in chat-oriented dialogue systems. Compared to human-human conversations and task-oriented dialogues, little is known about the errors made in chat-oriented dialogue systems. Through a data collection of chat dialogues and analyses of dialogue breakdowns, we classified errors and created a taxonomy. Although the proposed taxonomy may not be complete, this paper is the first to present a taxonomy of errors in chat-oriented dialogue systems. We also highlight the difficulty in pinpointing errors in such systems.
This paper aims to find errors that lead to dialogue breakdowns in chat-oriented dialogue systems. We collected chat dialogue data, annotated them with dialogue breakdown labels, and collected comments describing the error that led to the breakdown. By mining the comments, we first identified error types. Then, we calculated the correlation between an error type and the degree of dialogue breakdown it incurred, quantifying its impact on dialogue breakdown. This is the first study to quantitatively analyze error types and their effect in chat-oriented dialogue systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.