Sebastian Varges

Interactive question answering and constraint relaxation in spoken dialogue systems

We explore the relationship between question answering and constraint relaxation in spoken dialogue systems and develop dialogue strategies for selecting and presenting information succinctly. In particular, we describe methods for dealing with the results of database queries in information-seeking dialogues. Our goal is to structure the dialogue in such a way that the user is neither overwhelmed with information nor left uncertain as to how to refine the query further. We present two sets of evaluation results for a restaurant selection task: one is a system performance evaluation experiment involving twenty subjects, the other is an experimental evaluation of the use of suggestions involving sixteen subjects.

Generation of repeated references to discourse entities

Generation of Referring Expressions is a thriving subfield of Natural Language Generation which has traditionally focused on the task of selecting a set of attributes that unambiguously identify a given referent. In this paper, we address the complementary problem of generating repeated, potentially different referential expressions that refer to the same entity in the context of a piece of discourse longer than a sentence. We describe a corpus of short encyclopaedic texts we have compiled and annotated for reference to the main subject of the text, and report results for our experiments in which we set human subjects and automatic methods the task of selecting a referential expression from a wide range of choices in a full-text context. We find that our human subjects agree on choice of expression to a considerable degree, with three identical expressions selected in 50% of cases. We tested automatic selection strategies based on most frequent choice heuristics, involving different combinations of information about syntactic MSR type and domain type. We find that more information generally produces better results, achieving a best overall test set accuracy of 53.9% when both syntactic MSR type and domain type are known.

Leveraging POMDPs Trained with User Simulations and Rule-based Dialogue Management in a Spoken Dialogue System

We have developed a complete spoken dialogue framework that includes rule-based and trainable dialogue managers, speech recognition, spoken language understanding and generation modules, and a comprehensive web visualization interface. We present a spoken dialogue system based on Reinforcement Learning that goes beyond standard rule based models and computes on-line decisions of the best dialogue moves. Bridging the gap between handcrafted (e.g. rule-based) and adaptive (e.g. based on Partially Observable Markov Decision Processes - POMDP) dialogue models, this prototype is able to learn high rewarding policies in a number of dialogue situations.

POMDP concept policies and task structures for hybrid dialog management

We address several challenges for applying statistical dialog managers based on Partially Observable Markov Models to real world problems: to deal with large numbers of concepts, we use individual POMDP policies for each concept. To control the use of the concept policies, the dialog manager uses explicit task structures. The POMDP policies model the confusability of concepts at the value level. In contrast to previous work, we use explicit confusability statistics including confidence scores based on real world data in the POMDP models. Since data sparseness becomes a key issue for estimating these probabilities, we introduce a form of smoothing the observation probabilities that maintains the overall concept error rate. We evaluated three POMDP-based dialog systems and a rule-based one in a phone-based user evaluation in a tourist domain. The results show that a POMDP that uses confidence scores, in combination with an improved SLU module, achieves the highest concept precision.

Instance-based natural language generation

We investigate the use of instance-based ranking methods for surface realization in natural language generation. Our approach to instance-based natural language generation (IBNLG) employs two components: a rule system that ‘overgenerates’ a number of realization candidates from a meaning representation and an instance-based ranker that scores the candidates according to their similarity to examples taken from a training corpus. We develop an efficient search technique for identifying the optimal candidate based on a novel extension of the A* algorithm. The rule system is produced automatically from a semantically annotated fragment of the Penn Treebank II containing management succession texts. We detail the annotation scheme and grammar induction algorithm and evaluate the efficiency and output of the generator. We also discuss issues such as input coverage (completeness) and fluency that are relevant to surface generation in general.

The LUNA Spoken Dialogue System: Beyond utterance classification

We present a call routing application for complex problem solving tasks. Up to date work on call routing has been mainly dealing with call-type classification. In this paper we take call routing further: Initial call classification is done in parallel with a robust statistical Spoken Language Understanding module. This is followed by a dialogue to elicit further task-relevant details from the user before passing on the call. The dialogue capability also allows us to obtain clarifications of the initial classifier guess. Based on an evaluation, we show that conducting a dialogue significantly improves upon call routing based on call classification alone. We present both subjective and objective evaluation results of the system according to standard metrics on real users.

The exploration/exploitation trade-off in Reinforcement Learning for dialogue management

Conversational systems use deterministic rules that trigger actions such as requests for confirmation or clarification. More recently, Reinforcement Learning and (Partially Observable) Markov Decision Processes have been proposed for this task. In this paper, we investigate action selection strategies for dialogue management, in particular the exploration/exploitation trade-off and its impact on final reward (i.e. the session reward after optimization has ended) and lifetime reward (i.e. the overall reward accumulated over the learners lifetime). We propose to use interleaved exploitation sessions as a learning methodology to assess the reward obtained from the current policy. The experiments show a statistically significant difference in final reward of exploitation-only sessions between a system that optimizes lifetime reward and one that maximizes the reward of the final policy.

Combining POMDPs trained with User Simulations and Rule-based Dialogue Management in a Spoken Dialogue System

Over several years, we have developed an approach to spoken dialogue systems that includes rule-based and trainable dialogue managers, spoken language understanding and generation modules, and a comprehensive dialogue system architecture. We present a Reinforcement Learning-based dialogue system that goes beyond standard rule-based models and computes on-line decisions of the best dialogue moves. The key concept of this work is that we bridge the gap between manually written dialog models (e.g. rule-based) and adaptive computational models such as Partially Observable Markov Decision Processes (POMDP) based dialogue managers.