Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation

Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience. Although there are a set of prior approaches to out-of-domain (OOD) utterance detection, they share a few restrictions: they rely on OOD data or multiple sub-domains, and their OOD detection is context-independent which leads to suboptimal performance in a dialog. The goal of this paper is to propose a novel OOD detection method that does not require OOD data by utilizing counterfeit OOD turns in the context of a dialog. For the sake of fostering further research, we also release new dialog datasets which are 3 publicly available dialog corpora augmented with OOD turns in a controllable way. Our method outperforms state-of-the-art dialog models equipped with a conventional OOD detection mechanism by a large margin in the presence of OOD utterances.Comment: ICASSP 201

Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena

Natural, spontaneous dialogue proceeds incrementally on a word-by-word basis; and it contains many sorts of disfluency such as mid-utterance/sentence hesitations, interruptions, and self-corrections. But training data for machine learning approaches to dialogue processing is often either cleaned-up or wholly synthetic in order to avoid such phenomena. The question then arises of how well systems trained on such clean data generalise to real spontaneous dialogue, or indeed whether they are trainable at all on naturally occurring dialogue data. To answer this question, we created a new corpus called bAbI+ by systematically adding natural spontaneous incremental dialogue phenomena such as restarts and self-corrections to the Facebook AI Research's bAbI dialogues dataset. We then explore the performance of a state-of-the-art retrieval model, MemN2N, on this more natural dataset. Results show that the semantic accuracy of the MemN2N model drops drastically; and that although it is in principle able to learn to process the constructions in bAbI+, it needs an impractical amount of training data to do so. Finally, we go on to show that an incremental, semantic parser -- DyLan -- shows 100% semantic accuracy on both bAbI and bAbI+, highlighting the generalisation properties of linguistically informed dialogue models.Comment: 9 pages, 3 figures, 2 tables. Accepted as a full paper for SemDial 201

Neural Response Ranking for Social Conversation: A Data-Efficient Approach

The overall objective of 'social' dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart from raw dialogue data, user-provided ratings are the most common signal used to train such systems to produce engaging responses. In this paper we show that social dialogue systems can be trained effectively from raw unannotated data. Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good' system responses to user utterances, i.e. responses which are likely to lead to long and engaging conversations. We show that (1) our neural ranker consistently outperforms several strong baselines when trained to optimise for user ratings; (2) when trained on larger amounts of data and only using conversation length as the objective, the ranker performs better than the one trained using ratings -- ultimately reaching a Precision@1 of 0.87. This advance will make data collection for social conversational agents simpler and less expensive in the future.Comment: 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI. Brussels, Belgium, October 31, 201

Data-efficient methods for dialogue systems

Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa or more business-oriented customer support automation solutions. Deep learning underlies many recent breakthroughs in dialogue systems but requires very large amounts of training data, often annotated by experts — and this dramatically increases the cost of deploying such systems in production setups and reduces their flexibility as software products. Trained with smaller data, these methods end up severely lacking robustness to various phenomena of spoken language (e.g. disfluencies), out-of-domain input, and often just have too little generalisation power to other tasks and domains. In this thesis, we address the above issues by introducing a series of methods for bootstrapping robust dialogue systems from minimal data. Firstly, we study two orthogonal approaches to dialogue: a linguistically informed model (DyLan) and a machine learning-based one (MemN2N) — from the data efficiency perspective, i.e. their potential to generalise from minimal data and robustness to natural spontaneous input. We outline the steps to obtain data-efficient solutions with either approach and proceed with the neural models for the rest of the thesis. We then introduce the core contributions of this thesis, two data-efficient models for dialogue response generation: the Dialogue Knowledge Transfer Network (DiKTNet) based on transferable latent dialogue representations, and the Generative-Retrieval Transformer (GRTr) combining response generation logic with a retrieval mechanism as the fallback. GRTr ranked first at the Dialog System Technology Challenge 8 Fast Domain Adaptation task. Next, we the problem of training robust neural models from minimal data. As such, we look at robustness to disfluencies and propose a multitask LSTM-based model for domain-general disfluency detection. We then go on to explore robustness to anomalous, or out-of-domain (OOD) input. We address this problem by (1) presenting Turn Dropout, a data-augmentation technique facilitating training for anomalous input only using in-domain data, and (2) introducing VHCN and AE-HCN, autoencoder-augmented models for efficient training with turn dropout based on the Hybrid Code Networks (HCN) model family. With all the above work addressing goal-oriented dialogue, our final contribution in this thesis focuses on social dialogue where the main objective is maintaining natural, coherent, and engaging conversation for as long as possible. We introduce a neural model for response ranking in social conversation used in Alana, the 3rd place winner in the Amazon Alexa Prize 2017 and 2018. For our model, we employ a novel technique of predicting the dialogue length as the main objective for ranking. We show that this approach matches the performance of its counterpart based on the conventional, human rating-based objective — and surpasses it given more raw dialogue transcripts, thus reducing the dependence on costly and cumbersome dialogue annotations.EPSRC project BABBLE (grant EP/M01553X/1)

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

An Ensemble Model with Ranking for Social Dialogue

Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.Comment: NIPS 2017 Workshop on Conversational A