9,389 research outputs found
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
We present a novel response generation system that can be trained end to end
on large quantities of unstructured Twitter conversations. A neural network
architecture is used to address sparsity issues that arise when integrating
contextual information into classic statistical models, allowing the system to
take into account previous dialog utterances. Our dynamic-context generative
models show consistent gains over both context-sensitive and
non-context-sensitive Machine Translation and Information Retrieval baselines.Comment: A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell,
J.-Y. Nie, J. Gao, B. Dolan. 2015. A Neural Network Approach to
Context-Sensitive Generation of Conversational Responses. In Proc. of
NAACL-HLT. Pages 196-20
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
Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
Neural conversational models require substantial amounts of dialogue data for
their parameter estimation and are therefore usually learned on large corpora
such as chat forums or movie subtitles. These corpora are, however, often
challenging to work with, notably due to their frequent lack of turn
segmentation and the presence of multiple references external to the dialogue
itself. This paper shows that these challenges can be mitigated by adding a
weighting model into the architecture. The weighting model, which is itself
estimated from dialogue data, associates each training example to a numerical
weight that reflects its intrinsic quality for dialogue modelling. At training
time, these sample weights are included into the empirical loss to be
minimised. Evaluation results on retrieval-based models trained on movie and TV
subtitles demonstrate that the inclusion of such a weighting model improves the
model performance on unsupervised metrics.Comment: Accepted to SIGDIAL 201
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