10 research outputs found
Robust Dialog State Tracking for Large Ontologies
The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the
previous three editions as follows: the number of slot-value pairs present in
the ontology is much larger, no spoken language understanding output is given,
and utterances are labeled at the subdialog level. This paper describes a novel
dialog state tracking method designed to work robustly under these conditions,
using elaborate string matching, coreference resolution tailored for dialogs
and a few other improvements. The method can correctly identify many values
that are not explicitly present in the utterance. On the final evaluation, our
method came in first among 7 competing teams and 24 entries. The F1-score
achieved by our method was 9 and 7 percentage points higher than that of the
runner-up for the utterance-level evaluation and for the subdialog-level
evaluation, respectively.Comment: Paper accepted at IWSDS 201
Dialogue state tracking accuracy improvement by distinguishing slot-value pairs and dialogue behaviour
Dialog state tracking (DST) plays a critical role in cycle life of a task-oriented dialogue system. DST represents the goals of the consumer at each step by dialogue and describes such objectives as a conceptual structure comprising slot-value pairs and dialogue actions that specifically improve the performance and effectiveness of dialogue systems. DST faces several challenge
MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert
How to build a high-quality multi-domain dialogue system is a challenging
work due to its complicated and entangled dialogue state space among each
domain, which seriously limits the quality of dialogue policy, and further
affects the generated response. In this paper, we propose a novel method to
acquire a satisfying policy and subtly circumvent the knotty dialogue state
representation problem in the multi-domain setting. Inspired by real school
teaching scenarios, our method is composed of multiple domain-specific teachers
and a universal student. Each individual teacher only focuses on one specific
domain and learns its corresponding domain knowledge and dialogue policy based
on a precisely extracted single domain dialogue state representation. Then,
these domain-specific teachers impart their domain knowledge and policies to a
universal student model and collectively make this student model a multi-domain
dialogue expert. Experiment results show that our method reaches competitive
results with SOTAs in both multi-domain and single domain setting.Comment: AAAI 2020, Spotlight Pape
Retrieval-based Goal-Oriented Dialogue Generation
Most research on dialogue has focused either on dialogue generation for
openended chit chat or on state tracking for goal-directed dialogue. In this
work, we explore a hybrid approach to goal-oriented dialogue generation that
combines retrieval from past history with a hierarchical, neural
encoder-decoder architecture. We evaluate this approach in the customer support
domain using the Multiwoz dataset (Budzianowski et al., 2018). We show that
adding this retrieval step to a hierarchical, neural encoder-decoder
architecture leads to significant improvements, including responses that are
rated more appropriate and fluent by human evaluators. Finally, we compare our
retrieval-based model to various semantically conditioned models explicitly
using past dialog act information, and find that our proposed model is
competitive with the current state of the art (Chen et al., 2019), while not
requiring explicit labels about past machine acts
Spectral decomposition method of dialog state tracking via collective matrix factorization
Revised versionThe task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches
Recommended from our members
Discriminative methods for statistical spoken dialogue systems
Dialogue promises a natural and effective method for users to interact with and obtain information from computer systems. Statistical spoken dialogue systems are able to disambiguate in the presence of errors by maintaining probability distributions over what they believe to be the state of a dialogue. However, traditionally these distributions have been derived using generative models, which do not directly optimise for the criterion of interest and cannot easily exploit arbitrary information that may potentially be useful. This thesis presents how discriminative methods can overcome these problems in Spoken Language Understanding (SLU) and Dialogue State Tracking (DST).
A robust method for SLU is proposed, based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. This method uses discriminative classifiers, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus, and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance in terms of both accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the system's output.
For DST, a new word-based tracking method is presented that maps directly from the speech recognition results to the dialogue state without using an explicit semantic decoder. The method is based on a recurrent neural network structure that is capable of generalising to unseen dialogue state hypotheses, and requires very little feature engineering. The method is evaluated in the second and third Dialog State Tracking Challenges, as well as in a live user trial. The results demonstrate consistently high performance across all of the off-line metrics and a substantial increase in the quality of the dialogues in the live trial. The proposed method is shown to be readily applied to expanding dialogue domains, by exploiting robust features and a new method for online unsupervised adaptation. It is shown how the neural network structure can be adapted to output structured joint distributions, giving an improvement over estimating the dialogue state as a product of marginal distributions