39 research outputs found
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for
open-domain dialogue. It is trained using a unique combination of offline
two-phase supervised learning and online human-in-the-loop active learning.
While most existing research proposes offline supervision or hand-crafted
reward functions for online reinforcement, we devise a novel interactive
learning mechanism based on hamming-diverse beam search for response generation
and one-character user-feedback at each step. Experiments show that our model
inherently promotes the generation of semantically relevant and interesting
responses, and can be used to train agents with customized personas, moods and
conversational styles.Comment: Accepted at 6th Joint Conference on Lexical and Computational
Semantics (*SEM) 2017 (Previously titled "Online Sequence-to-Sequence Active
Learning for Open-Domain Dialogue Generation" on ArXiv
Learning to execute or ask clarification questions
Collaborative tasks are ubiquitous activities where a form of communication is required in order to reach a joint goal. Collaborative building is one of such tasks. We wish to develop an intelligent builder agent in a simulated building environment (Minecraft) that can build whatever users wish to build by just talking to the agent. In order to achieve this goal, such agents need to be able to take the initiative by asking clarification questions when further information is needed. Existing works on Minecraft Corpus Dataset only learn to execute instructions neglecting the importance of asking for clarifications. In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions. Experimental results show that our model achieves state-of-the-art performance on the collaborative building task with a substantial improvement. We also define two new tasks, the learning to ask task and the joint learning task. The latter consists of solving both collaborating building and learning to ask tasks jointly
Ericson: An Interactive Open-Domain Conversational Search Agent
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date
information, while maintaining natural conversations to help users refine and
ultimately answer information needs. However, creating an effective and robust
ODCS agent is challenging. In this paper, we present a fully functional ODCS
system, Ericson, which includes state-of-the-art question answering and
information retrieval components, as well as intent inference and dialogue
management models for proactive question refinement and recommendations. Our
system was stress-tested in the Amazon Alexa Prize, by engaging in live
conversations with thousands of Alexa users, thus providing empirical basis for
the analysis of the ODCS system in real settings. Our interaction data analysis
revealed that accurate intent classification, encouraging user engagement, and
careful proactive recommendations contribute most to the users satisfaction.
Our study further identifies limitations of the existing search techniques, and
can serve as a building block for the next generation of ODCS agents.Comment: pre-prin