1,351 research outputs found
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system
(SDS) usually needs a substantial amount of handcrafting or a well-labeled
dataset to be trained on. These limitations add significantly to development
costs and make cross-domain, multi-lingual dialogue systems intractable.
Moreover, human languages are context-aware. The most natural response should
be directly learned from data rather than depending on predefined syntaxes or
rules. This paper presents a statistical language generator based on a joint
recurrent and convolutional neural network structure which can be trained on
dialogue act-utterance pairs without any semantic alignments or predefined
grammar trees. Objective metrics suggest that this new model outperforms
previous methods under the same experimental conditions. Results of an
evaluation by human judges indicate that it produces not only high quality but
linguistically varied utterances which are preferred compared to n-gram and
rule-based systems.Comment: To be appear in SigDial 201
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the
sequence-to-sequence framework to model natural language generation as two
parallel discrete stochastic processes: a sequence of high-level coarse tokens,
and a sequence of natural language tokens. There are many ways to estimate or
learn the high-level coarse tokens, but we argue that a simple extraction
procedure is sufficient to capture a wealth of high-level discourse semantics.
Such procedure allows training the multiresolution recurrent neural network by
maximizing the exact joint log-likelihood over both sequences. In contrast to
the standard log- likelihood objective w.r.t. natural language tokens (word
perplexity), optimizing the joint log-likelihood biases the model towards
modeling high-level abstractions. We apply the proposed model to the task of
dialogue response generation in two challenging domains: the Ubuntu technical
support domain, and Twitter conversations. On Ubuntu, the model outperforms
competing approaches by a substantial margin, achieving state-of-the-art
results according to both automatic evaluation metrics and a human evaluation
study. On Twitter, the model appears to generate more relevant and on-topic
responses according to automatic evaluation metrics. Finally, our experiments
demonstrate that the proposed model is more adept at overcoming the sparsity of
natural language and is better able to capture long-term structure.Comment: 21 pages, 2 figures, 10 table
EmoUS: Simulating User Emotions in Task-Oriented Dialogues
Existing user simulators (USs) for task-oriented dialogue systems only model
user behaviour on semantic and natural language levels without considering the
user persona and emotions. Optimising dialogue systems with generic user
policies, which cannot model diverse user behaviour driven by different
emotional states, may result in a high drop-off rate when deployed in the real
world. Thus, we present EmoUS, a user simulator that learns to simulate user
emotions alongside user behaviour. EmoUS generates user emotions, semantic
actions, and natural language responses based on the user goal, the dialogue
history, and the user persona. By analysing what kind of system behaviour
elicits what kind of user emotions, we show that EmoUS can be used as a probe
to evaluate a variety of dialogue systems and in particular their effect on the
user's emotional state. Developing such methods is important in the age of
large language model chat-bots and rising ethical concerns.Comment: accepted by SIGIR202
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