8,494 research outputs found
Scalable Multi-Domain Dialogue State Tracking
Dialogue state tracking (DST) is a key component of task-oriented dialogue
systems. DST estimates the user's goal at each user turn given the interaction
until then. State of the art approaches for state tracking rely on deep
learning methods, and represent dialogue state as a distribution over all
possible slot values for each slot present in the ontology. Such a
representation is not scalable when the set of possible values are unbounded
(e.g., date, time or location) or dynamic (e.g., movies or usernames).
Furthermore, training of such models requires labeled data, where each user
turn is annotated with the dialogue state, which makes building models for new
domains challenging. In this paper, we present a scalable multi-domain deep
learning based approach for DST. We introduce a novel framework for state
tracking which is independent of the slot value set, and represent the dialogue
state as a distribution over a set of values of interest (candidate set)
derived from the dialogue history or knowledge. Restricting these candidate
sets to be bounded in size addresses the problem of slot-scalability.
Furthermore, by leveraging the slot-independent architecture and transfer
learning, we show that our proposed approach facilitates quick adaptation to
new domains.Comment: Published at ASRU-17. New version has updated results in Tables 1, 2
and 3 corresponding to the datasets released on
github.com/google-research-datasets/simulated-dialogu
The SPPD System for Schema Guided Dialogue State Tracking Challenge
This paper introduces one of our group's work on the Dialog System Technology
Challenges 8 (DSTC8), the SPPD system for Schema Guided dialogue state tracking
challenge. This challenge, named as Track 4 in DSTC8, provides a brand new and
challenging dataset for developing scalable multi-domain dialogue state
tracking algorithms for real world dialogue systems. We propose a zero-shot
dialogue state tracking system for this task. The key components of the system
is a number of BERT based zero-shot NLU models that can effectively capture
semantic relations between natural language descriptions of services' schemas
and utterances from dialogue turns. We also propose some strategies to make the
system better to exploit information from longer dialogue history and to
overcome the slot carryover problem for multi-domain dialogues. The
experimental results show that the proposed system achieves a significant
improvement compared with the baseline system
Toward Scalable Neural Dialogue State Tracking Model
The latency in the current neural based dialogue state tracking models
prohibits them from being used efficiently for deployment in production
systems, albeit their highly accurate performance. This paper proposes a new
scalable and accurate neural dialogue state tracking model, based on the
recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et
al. which uses global modules to share parameters between estimators for
different types (called slots) of dialogue states, and uses local modules to
learn slot-specific features. By using only one recurrent networks with global
conditioning, compared to (1 + \# slots) recurrent networks with global and
local conditioning used in the GLAD model, our proposed model reduces the
latency in training and inference times by on average, while preserving
performance of belief state tracking, by on turn request and
on joint goal and accuracy. Evaluation on Multi-domain dataset
(Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform
and joint goal accuracy.Comment: 32nd Conference on Neural Information Processing Systems (NeurIPS
2018), 2nd Conversational AI workshop, Montr\'eal, Canad
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation
We present a novel approach to dialogue state tracking and referring
expression resolution tasks. Successful contextual understanding of multi-turn
spoken dialogues requires resolving referring expressions across turns and
tracking the entities relevant to the conversation across turns. Tracking
conversational state is particularly challenging in a multi-domain scenario
when there exist multiple spoken language understanding (SLU) sub-systems, and
each SLU sub-system operates on its domain-specific meaning representation.
While previous approaches have addressed the disparate schema issue by learning
candidate transformations of the meaning representation, in this paper, we
instead model the reference resolution as a dialogue context-aware user query
reformulation task -- the dialog state is serialized to a sequence of natural
language tokens representing the conversation. We develop our model for query
reformulation using a pointer-generator network and a novel multi-task learning
setup. In our experiments, we show a significant improvement in absolute F1 on
an internal as well as a, soon to be released, public benchmark respectively.Comment: Accepted to NAACL 201
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
Dialogue systems dealing with multi-domain tasks are highly required. How to
record the state remains a key problem in a task-oriented dialogue system.
Normally we use human-defined features as dialogue states and apply a state
tracker to extract these features. However, the performance of such a system is
limited by the error propagation of a state tracker. In this paper, we propose
a dialogue generation model that needs no external state trackers and still
benefits from human-labeled semantic data. By using a teacher-student
framework, several teacher models are firstly trained in their individual
domains, learn dialogue policies from labeled states. And then the learned
knowledge and experience are merged and transferred to a universal student
model, which takes raw utterance as its input. Experiments show that the
dialogue system trained under our framework outperforms the one uses a belief
tracker.Comment: Official Version: arXiv:2005.1045
Efficient Dialogue State Tracking by Selectively Overwriting Memory
Recent works in dialogue state tracking (DST) focus on an open
vocabulary-based setting to resolve scalability and generalization issues of
the predefined ontology-based approaches. However, they are inefficient in that
they predict the dialogue state at every turn from scratch. Here, we consider
dialogue state as an explicit fixed-sized memory and propose a selectively
overwriting mechanism for more efficient DST. This mechanism consists of two
steps: (1) predicting state operation on each of the memory slots, and (2)
overwriting the memory with new values, of which only a few are generated
according to the predicted state operations. Our method decomposes DST into two
sub-tasks and guides the decoder to focus only on one of the tasks, thus
reducing the burden of the decoder. This enhances the effectiveness of training
and DST performance. Our SOM-DST (Selectively Overwriting Memory for Dialogue
State Tracking) model achieves state-of-the-art joint goal accuracy with 51.72%
in MultiWOZ 2.0 and 53.01% in MultiWOZ 2.1 in an open vocabulary-based DST
setting. In addition, we analyze the accuracy gaps between the current and the
ground truth-given situations and suggest that it is a promising direction to
improve state operation prediction to boost the DST performance.Comment: Accepted to ACL2020 as a long pape
Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing
Robust dialogue belief tracking is a key component in maintaining good
quality dialogue systems. The tasks that dialogue systems are trying to solve
are becoming increasingly complex, requiring scalability to multi domain,
semantically rich dialogues. However, most current approaches have difficulty
scaling up with domains because of the dependency of the model parameters on
the dialogue ontology. In this paper, a novel approach is introduced that fully
utilizes semantic similarity between dialogue utterances and the ontology
terms, allowing the information to be shared across domains. The evaluation is
performed on a recently collected multi-domain dialogues dataset, one order of
magnitude larger than currently available corpora. Our model demonstrates great
capability in handling multi-domain dialogues, simultaneously outperforming
existing state-of-the-art models in single-domain dialogue tracking tasks.Comment: 10 pages, 1 figure and 2 tables. In Proceedings of the 56th Annual
Meeting of the Association for Computational Linguistics (ACL
Modeling Long Context for Task-Oriented Dialogue State Generation
Based on the recently proposed transferable dialogue state generator (TRADE)
that predicts dialogue states from utterance-concatenated dialogue context, we
propose a multi-task learning model with a simple yet effective utterance
tagging technique and a bidirectional language model as an auxiliary task for
task-oriented dialogue state generation. By enabling the model to learn a
better representation of the long dialogue context, our approaches attempt to
solve the problem that the performance of the baseline significantly drops when
the input dialogue context sequence is long. In our experiments, our proposed
model achieves a 7.03% relative improvement over the baseline, establishing a
new state-of-the-art joint goal accuracy of 52.04% on the MultiWOZ 2.0 dataset.Comment: ACL 202
A Survey on Dialog Management: Recent Advances and Challenges
Dialog management (DM) is a crucial component in a task-oriented dialog
system. Given the dialog history, DM predicts the dialog state and decides the
next action that the dialog agent should take. Recently, dialog policy learning
has been widely formulated as a Reinforcement Learning (RL) problem, and more
works focus on the applicability of DM. In this paper, we survey recent
advances and challenges within three critical topics for DM: (1) improving
model scalability to facilitate dialog system modeling in new scenarios, (2)
dealing with the data scarcity problem for dialog policy learning, and (3)
enhancing the training efficiency to achieve better task-completion performance
. We believe that this survey can shed a light on future research in dialog
management
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
Zero-shot transfer learning for multi-domain dialogue state tracking can
allow us to handle new domains without incurring the high cost of data
acquisition. This paper proposes new zero-short transfer learning technique for
dialogue state tracking where the in-domain training data are all synthesized
from an abstract dialogue model and the ontology of the domain. We show that
data augmentation through synthesized data can improve the accuracy of
zero-shot learning for both the TRADE model and the BERT-based SUMBT model on
the MultiWOZ 2.1 dataset. We show training with only synthesized in-domain data
on the SUMBT model can reach about 2/3 of the accuracy obtained with the full
training dataset. We improve the zero-shot learning state of the art on average
across domains by 21%.Comment: 9 pages. To appear in ACL 202
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