The Situated Interactive Multi-Modal Conver-001 sations (SIMMC) 2.0 aims to create virtual 002 shopping assistants that can accept complex 003 multi-modal inputs, i.e. visual appearances of 004 objects and user utterances. It consists of four 005 subtasks, multi-modal disambiguation (MM-006 Disamb), multi-modal coreference resolution 007 (MM-Coref), multi-modal dialog state tracking 008 (MM-DST), and response retrieval and genera-009 tion. While many task-oriented dialog systems 010 usually tackle each subtask separately, we pro-011 pose a jointly learned encoder-decoder that per-012 forms all four subtasks at once for efficiency. 013 Moreover, we handle the multi-modality of the 014 challenge by representing visual objects as spe-015 cial tokens whose joint embedding is learned 016 via auxiliary tasks. This approach won the MM-017 Coref and response retrieval subtasks and nom-018 inated runner-up for the remaining subtasks 019 using a single unified model. In particular, 020 our model achieved 81.5% MRR, 71.2% R@1, 021 95.0% R@5, 98.2% R@10, and 1.9 mean rank 022 in response retrieval task, setting a high bar for 023 the state-of-the-art result in the SIMMC 2.0 024 track of the Dialog Systems Technology Chal-025 lenge 10 (DSTC10). 026

Recently, many BERT based approaches have 001 been proposed for task-oriented dialogue 002 (TOD) task. Despite their impressive perfor-003 mance, the insufficient utilization of deep se-004 mantic information and long-distance context 005 understanding makes it difficult for these meth-006 ods to digest complex dialogue scenarios for 007 they cannot obtain sufficient evidence from dia-008 logue data to support dialogue decision-making. 009 In this work, we propose a novel structured se-010 mantics reinforcement (SSR) method to handle 011 these issues. SSR reorganized the end-to-end 012 TOD structure, which mainly includes two key 013 components: 1. The dialogue symbolic mem-014 ory, which cache the objects mentioned in the 015 dialogue and the structure under the seman-016 tic relationship. 2. semantic projection mod-017 ule, understanding module, based on the pre-018 vious structured results, determines the source 019 of the slot extraction required for the current 020 task. And our approach achieves state-of-the-021 art results on dataset MultiWOZ 2.1, where 022 we acquire a joint goal accuracy beyond 60% 023 and also gains a significant effect on dataset 024 DSTC8. 025

Three complementary feature extraction approaches are developed in this thesis which addresses the challenge of dimensionality reduction in the presence of multivariate heavy-tailed and asymmetric distributions. First, we demonstrate how to improve the robustness of the standard Probabilistic Principal Component Analysis by adapting the concept of robust mean and covariance estimation within the standard framework. We then introduce feature extraction methods that extend the standard Principal Component Analysis by exploring distribution-based robustification. This is achieved via Probabilistic Principal Component Analysis (PPCA), in which new, statistically robust variants are derived, also treating missing data. We propose a novel generalisation to the t-Student Probabilistic Principal Component methodology which (1) accounts for asymmetric distribution of the observation data, (2) is a framework for grouped and generalised multiple-degree-of-freedom structures, which provides a more flexible framework to model groups of marginal tail dependence in the observation data, and (3) separates the tail effect of the error terms and factors. The new feature extraction methods are derived in an incomplete data setting to efficiently handle the presence of missing values in the observation vector. We discuss statistical properties of their robustness. In the next part of this thesis, we demonstrate the applicability of feature extraction methods to the statistical analysis of multidimensional dynamics. We introduce the class of Hybrid Factor models that combines classical state-space model formulations with incorporation of exogenous factors. We show how to utilize the information obtained from features extracted using introduced robust PPCA in a modelling framework in a meaningful and parsimonious manner. In the first application study, we show the applicability of robust feature extraction methods in the real data environment of financial markets and combine the obtained results with a stochastic multi-factor panel regression-based state-space model in order to model the dynamic of yield curves, whilst incorporating regression factors. We embed the rank-reduced feature extractions into a stochastic representation of state-space models for yield curve dynamics and compare the results to classical multi-factor dynamic Nelson-Siegel state-space models. This leads to important new representations of yield curve models that can have practical importance for addressing questions of financial stress testing and monetary policy interventions which can

One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user’s goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users’ language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.

In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. We also show how the method can be used to tailor the word vector space for the downstream task of dialogue state tracking, resulting in robust improvements across different dialogue domains.

Dialog state tracking is a key component of many modern dialog systems, most of which are designed with a single, well-defined domain in mind. This paper shows that dialog data drawn from different dialog domains can be used to train a general belief tracking model which can operate across all of these domains, exhibiting superior performance to each of the domain-specific models. We propose a training procedure which uses out-of-domain data to initialise belief tracking models for entirely new domains. This procedure leads to improvements in belief tracking performance regardless of the amount of in-domain data available for training the model.

A spoken dialog system, while communicating with a user, must keep track of what the user wants from the system at each step.This process, termed dialog state tracking, is essential for a successful dialog system as it directly informs the system's actions.The first Dialog State Tracking Challenge allowed for evaluation of different dialog state tracking techniques, providing common testbeds and evaluation suites.This paper presents a second challenge, which continues this tradition and introduces some additional features -a new domain, changing user goals and a richer dialog state.The challenge received 31 entries from 9 research groups.The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions.An investigation into ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers.

Tracking the user's intention throughout the course of a dialog, called dialog state tracking, is an important component of any dialog system. Most existing spoken dialog systems are designed to work in a static, well-defined domain, and are not well suited to tasks in which the domain may change or be extended over time. This paper shows how recurrent neural networks can be effectively applied to tracking in an extended domain with new slots and values not present in training data. The method is evaluated in the third Dialog State Tracking Challenge, where it significantly outperforms other approaches in the task of tracking the user's goal. A method for online unsupervised adaptation to new domains is also presented. Unsupervised adaptation is shown to be helpful in improving word-based recurrent neural networks, which work directly from the speech recognition results. Word-based dialog state tracking is attractive as it does not require engineering a spoken language understanding system for use in the new domain and it avoids the need for a general purpose intermediate semantic representation.

The focus of this work is speech synthesis tailored to the needs of spoken dialogue systems. More specifically, the framework of HMM-based speech synthesis is utilized to train an emphatic voice that also considers dialogue context for decision tree state clustering. To achieve this, we designed and recorded a speech corpus comprising system prompts from human-computer interaction, as well as additional prompts for slot-level emphasis. This corpus, combined with a general purpose text-to-speech one, was used to train voices using a) baseline context features, b) additional emphasis features, and c) additional dialogue context features. Both emphasis and dialogue context features are extracted from the dialogue act semantic representation. The voices were evaluated in pairs for dialogue appropriateness using a preference listening test. The results show that the emphatic voice is preferred to the baseline when emphasis markup is present, while the dialogue context-sensitive voice is preferred to the plain emphatic one when no emphasis markup is present and preferable to the baseline in both cases. This demonstrates that including dialogue context features for decision tree state clustering significantly improves the quality of the synthetic voice for dialogue.

Recently discriminative methods for tracking the state of a spoken dialog have been shown to outperform traditional generative models.This paper presents a new wordbased tracking method which maps directly from the speech recognition results to the dialog state without using an explicit semantic decoder.The method is based on a recurrent neural network structure which is capable of generalising to unseen dialog state hypotheses, and which requires very little feature engineering.The method is evaluated on the second Dialog State Tracking Challenge (DSTC2) corpus and the results demonstrate consistently high performance across all of the metrics.

In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking and also to deeper understanding about the problem itself, including methods for evaluation.

Copyright © 2014 ISCA. An important property of open domain spoken dialogue systems is their ability to deal with a set of new, previously unseen, concepts introduced in the conversation. The dialogue manager must then quickly learn how to talk about the new concepts using its knowledge of the existing concepts. It has previously been shown that a single new concept could be accommodated by mapping the kernel function of a Gaussian process to incorporate an additional concept into the domain of a statistical dialogue manager. Here we present an incremental scheme which enables the domain of a dialogue manager to be repeatedly extended by recursively specifying priors in Gaussian processes. We show that it is possible to effectively double the number of concepts understood by a system providing restaurant information using only 1000 adaptation dialogues with real users.

In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. This task is challenging because of speech recognition and language understanding errors, yet good dialog state tracking is crucial to the performance of spoken dialog systems. This paper presents results from the third Dialog State Tracking Challenge, a research community challenge task based on a corpus of annotated logs of human-computer dialogs, with a blind test set evaluation. The main new feature of this challenge is that it studied the ability of trackers to generalize to new entities - i.e. new slots and values not present in the training data. This challenge received 28 entries from 7 research teams. About half the teams substantially exceeded the performance of a competitive rule-based baseline, illustrating not only the merits of statistical methods for dialog state tracking but also the difficulty of the problem.

We demonstrate a mobile application in English and Mandarin to test and evaluate components of the Parlance dialogue system for interactive search under real-world conditions.

Compared to conventional hand-crafted rule-based dialogue management systems, statistical POMDP-based dialogue managers offer the promise of increased robustness, reduced development and maintenance costs, and scaleability to large open-domains. As a consequence, there has been considerable research activity in approaches to statistical spoken dialogue systems over recent years. However, building and deploying a real-time spoken dialogue system is expensive, and even when operational, it is hard to recruit sufficient users to get statistically significant results. Instead, researchers have tended to evaluate using user simulators or by reprocessing existing corpora, both of which are unconvincing predictors of actual real world performance. This paper describes the deployment of a real-world restaurant information system and its evaluation in a motor car using subjects recruited locally and by remote users recruited using Amazon Mechanical Turk. The paper explores three key questions: are statistical dialogue systems more robust than conventional hand-crafted systems; how does the performance of a system evaluated on a user simulator compare to performance with real users; and can performance of a system tested over the telephone network be used to predict performance in more hostile environments such as a motor car? The results show that the statistical approach is indeed more robust, but results from a simulator significantly over-estimate performance both absolute and relative. Finally, by matching WER rates, performance results obtained over the telephone can provide useful predictors of performance in noisier environments such as the motor car, but again they tend to over-estimate performance.

A partially observable Markov decision process has been proposed as a dialogue model that enables robustness to speech recognition errors and automatic policy optimisation using reinforcement learning (RL). However, conventional RL algorithms require a very large number of dialogues, necessitating a user simulator. Recently, Gaussian processes have been shown to substantially speed up the optimisation, making it possible to learn directly from interaction with human users. However, early studies have been limited to very low dimensional spaces and the learning has exhibited convergence problems. Here we investigate learning from human interaction using the Bayesian Update of Dialogue State system. This dynamic Bayesian network based system has an optimisation space covering more than one hundred features, allowing a wide range of behaviours to be learned. Using an improved policy model and a more robust reward function, we show that stable learning can be achieved that significantly outperforms a simulator trained policy.

Spoken dialogue systems provide a convenient way for users to interact with a machine using only speech. However, they often rely on a rigid turn taking regime in which a voice activity detection (VAD) module is used to determine when the user is speaking and decide when is an appropriate time for the system to respond. This paper investigates replacing the VAD and discrete utterance recogniser of a conventional turn-taking system with a continuously operating recogniser that is always listening, and using the recogniser 1-best path to guide turn taking. In this way, a flexible framework for incremental dialogue management is possible. Experimental results show that it is possible to remove the VAD component and successfully use the recogniser best path to identify user speech, with more robustness to noise, potentially smaller latency times, and a reduction in overall recognition error rate compared to using the conventional approach.

The Parlance system for interactive search processes dialogue at a microturn level, displaying dialogue phenomena that play a vital role in human spoken conversation. These dialogue phenomena include more natural turn-taking through rapid system responses, generation of backchannels, and user barge-ins. The Parlance demonstration system dierentiates from other incremental systems in that it is data-driven with an infrastructure that scales well.