7,873 research outputs found
Building a robust dialogue system with limited data
We describe robustness techniques used in the CommandTalk system at the recognition level, the parsing level, and th dia6ue level, and how these were influenced by the lack of domain data. We used interviews with subject matter experts (SME's) to develop a single grammar for recognition, understanding, and generation, thus eliminating the need for a robust parser. We broadened the coverage of the recognition grammar by allowing word insertions and deletions, and we implemented clarification and correction subdialogues to increase robustness at tte dialogue level. We discuss the applicability of these techniques to other domains
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings
We motivate and describe a new freely available human-human dialogue dataset
for interactive learning of visually grounded word meanings through ostensive
definition by a tutor to a learner. The data has been collected using a novel,
character-by-character variant of the DiET chat tool (Healey et al., 2003;
Mills and Healey, submitted) with a novel task, where a Learner needs to learn
invented visual attribute words (such as " burchak " for square) from a tutor.
As such, the text-based interactions closely resemble face-to-face conversation
and thus contain many of the linguistic phenomena encountered in natural,
spontaneous dialogue. These include self-and other-correction, mid-sentence
continuations, interruptions, overlaps, fillers, and hedges. We also present a
generic n-gram framework for building user (i.e. tutor) simulations from this
type of incremental data, which is freely available to researchers. We show
that the simulations produce outputs that are similar to the original data
(e.g. 78% turn match similarity). Finally, we train and evaluate a
Reinforcement Learning dialogue control agent for learning visually grounded
word meanings, trained from the BURCHAK corpus. The learned policy shows
comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17
Using dialogue to learn math in the LeActiveMath project
We describe a tutorial dialogue system under development that assists students in learning how to differentiate equations. The system uses deep natural language understanding and generation to both interpret students â utterances and automatically generate a response that is both mathematically correct and adapted pedagogically and linguistically to the local dialogue context. A domain reasoner provides the necessary knowledge about how students should approach math problems as well as their (in)correctness, while a dialogue manager directs pedagogical strategies and keeps track of what needs to be done to keep the dialogue moving along.
Follow-up question handling in the IMIX and Ritel systems: A comparative study
One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it
End-to-end optimization of goal-driven and visually grounded dialogue systems
End-to-end design of dialogue systems has recently become a popular research
topic thanks to powerful tools such as encoder-decoder architectures for
sequence-to-sequence learning. Yet, most current approaches cast human-machine
dialogue management as a supervised learning problem, aiming at predicting the
next utterance of a participant given the full history of the dialogue. This
vision is too simplistic to render the intrinsic planning problem inherent to
dialogue as well as its grounded nature, making the context of a dialogue
larger than the sole history. This is why only chit-chat and question answering
tasks have been addressed so far using end-to-end architectures. In this paper,
we introduce a Deep Reinforcement Learning method to optimize visually grounded
task-oriented dialogues, based on the policy gradient algorithm. This approach
is tested on a dataset of 120k dialogues collected through Mechanical Turk and
provides encouraging results at solving both the problem of generating natural
dialogues and the task of discovering a specific object in a complex picture
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