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

Recurrent Neural Network Language Generation for Dialogue Systems

Language is the principal medium for ideas, while dialogue is the most natural and effective way for humans to interact with and access information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses without the natural variation of human language. However, the frequent repetition of identical output forms can quickly make dialogue become tedious for most real-world users. Additionally, these rules and heuristics are not scalable and hence not trivially extensible to other domains or languages. A statistical approach to language generation can learn language decisions directly from data without relying on hand-coded rules or heuristics, which brings scalability and flexibility to NLG. Statistical models also provide an opportunity to learn in-domain human colloquialisms and cross-domain model adaptations. A robust and quasi-supervised NLG model is proposed in this thesis. The model leverages a Recurrent Neural Network (RNN)-based surface realiser and a gating mechanism applied to input semantics. The model is motivated by the Long-Short Term Memory (LSTM) network. The RNN-based surface realiser and gating mechanism use a neural network to learn end-to-end language generation decisions from input dialogue act and sentence pairs; it also integrates sentence planning and surface realisation into a single optimisation problem. The single optimisation not only bypasses the costly intermediate linguistic annotations but also generates more natural and human-like responses. Furthermore, a domain adaptation study shows that the proposed model can be readily adapted and extended to new dialogue domains via a proposed recipe. Continuing the success of end-to-end learning, the second part of the thesis speculates on building an end-to-end dialogue system by framing it as a conditional generation problem. The proposed model encapsulates a belief tracker with a minimal state representation and a generator that takes the dialogue context to produce responses. These features suggest comprehension and fast learning. The proposed model is capable of understanding requests and accomplishing tasks after training on only a few hundred human-human dialogues. A complementary Wizard-of-Oz data collection method is also introduced to facilitate the collection of human-human conversations from online workers. The results demonstrate that the proposed model can talk to human judges naturally, without any difficulty, for a sample application domain. In addition, the results also suggest that the introduction of a stochastic latent variable can help the system model intrinsic variation in communicative intention much better.Tsung-Hsien Wen's Ph.D. is supported by Toshiba Research Europe Ltd, Cambridge Research Laborator

Dialogue context sensitive HMM-based speech synthesis

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. © 2014 IEEE