2,013 research outputs found
Data-efficient goal-oriented conversation with dialogue knowledge transfer networks
Goal-oriented dialogue systems are now being widely adopted in industry where
it is of key importance to maintain a rapid prototyping cycle for new products
and domains. Data-driven dialogue system development has to be adapted to meet
this requirement --- therefore, reducing the amount of data and annotations
necessary for training such systems is a central research problem.
In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet),
a state-of-the-art approach to goal-oriented dialogue generation which only
uses a few example dialogues (i.e. few-shot learning), none of which has to be
annotated. We achieve this by performing a 2-stage training. Firstly, we
perform unsupervised dialogue representation pre-training on a large source of
goal-oriented dialogues in multiple domains, the MetaLWOz corpus. Secondly, at
the transfer stage, we train DiKTNet using this representation together with 2
other textual knowledge sources with different levels of generality: ELMo
encoder and the main dataset's source domains.
Our main dataset is the Stanford Multi-Domain dialogue corpus. We evaluate
our model on it in terms of BLEU and Entity F1 scores, and show that our
approach significantly and consistently improves upon a series of baseline
models as well as over the previous state-of-the-art dialogue generation model,
ZSDG. The improvement upon the latter --- up to 10% in Entity F1 and the
average of 3% in BLEU score --- is achieved using only the equivalent of 10% of
ZSDG's in-domain training data.Comment: EMNLP 201
MALA: Cross-Domain Dialogue Generation with Action Learning
Response generation for task-oriented dialogues involves two basic
components: dialogue planning and surface realization. These two components,
however, have a discrepancy in their objectives, i.e., task completion and
language quality. To deal with such discrepancy, conditioned response
generation has been introduced where the generation process is factorized into
action decision and language generation via explicit action representations. To
obtain action representations, recent studies learn latent actions in an
unsupervised manner based on the utterance lexical similarity. Such an action
learning approach is prone to diversities of language surfaces, which may
impinge task completion and language quality. To address this issue, we propose
multi-stage adaptive latent action learning (MALA) that learns semantic latent
actions by distinguishing the effects of utterances on dialogue progress. We
model the utterance effect using the transition of dialogue states caused by
the utterance and develop a semantic similarity measurement that estimates
whether utterances have similar effects. For learning semantic actions on
domains without dialogue states, MsALA extends the semantic similarity
measurement across domains progressively, i.e., from aligning shared actions to
learning domain-specific actions. Experiments using multi-domain datasets, SMD
and MultiWOZ, show that our proposed model achieves consistent improvements
over the baselines models in terms of both task completion and language
quality.Comment: 9 pages, 3 figure
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