Machine-Learned Ranking Based Non-Task-Oriented Dialogue Agent Using Twitter Data. Abstract: This paper describes a method for developing a non-task-oriented ...

Our method extracts a topic from a user's utterance and acquires candidate utterances that contain the topic from Twitter. Our agent selects a suitable ...

This paper presents a proposal of a construction method for non-task-oriented dialogue agents (chatbots) that are based on the statistical response method. The ...

This paper provides a novel method for building non-task-oriented dialogue agents such as chatbots that automatically selects a suitable utterance depending ...

This paper addresses the problem of utterance generation for non-task-oriented dialogue systems. We search twitter data by topic words and acquire sentences.

Experimental re-sults show that the proposed NUR model ranks utterances with higher precision relative to deep learning and other existing meth-ods.

Sep 13, 2016 · In this study, we present our neural ut- terance ranking (NUR) model, an utter- ance selection model for conversational dialogue agents.

Dec 6, 2015 · Analysis of User Behavior on Private Chat System pp. 1-4 ; Machine-Learned Ranking Based Non-Task-Oriented Dialogue Agent Using Twitter Data pp.

In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent ...

Dec 22, 2021 · The ratings based on human judgment are then used as target labels to learn an evaluation model based on objectively measurable performance ...