9,675 research outputs found
Predicting Causes of Reformulation in Intelligent Assistants
Intelligent assistants (IAs) such as Siri and Cortana conversationally
interact with users and execute a wide range of actions (e.g., searching the
Web, setting alarms, and chatting). IAs can support these actions through the
combination of various components such as automatic speech recognition, natural
language understanding, and language generation. However, the complexity of
these components hinders developers from determining which component causes an
error. To remove this hindrance, we focus on reformulation, which is a useful
signal of user dissatisfaction, and propose a method to predict the
reformulation causes. We evaluate the method using the user logs of a
commercial IA. The experimental results have demonstrated that features
designed to detect the error of a specific component improve the performance of
reformulation cause detection.Comment: 11 pages, 2 figures, accepted as a long paper for SIGDIAL 201
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.
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
The Speech-Language Interface in the Spoken Language Translator
The Spoken Language Translator is a prototype for practically useful systems
capable of translating continuous spoken language within restricted domains.
The prototype system translates air travel (ATIS) queries from spoken English
to spoken Swedish and to French. It is constructed, with as few modifications
as possible, from existing pieces of speech and language processing software.
The speech recognizer and language understander are connected by a fairly
conventional pipelined N-best interface. This paper focuses on the ways in
which the language processor makes intelligent use of the sentence hypotheses
delivered by the recognizer. These ways include (1) producing modified
hypotheses to reflect the possible presence of repairs in the uttered word
sequence; (2) fast parsing with a version of the grammar automatically
specialized to the more frequent constructions in the training corpus; and (3)
allowing syntactic and semantic factors to interact with acoustic ones in the
choice of a meaning structure for translation, so that the acoustically
preferred hypothesis is not always selected even if it is within linguistic
coverage.Comment: 9 pages, LaTeX. Published: Proceedings of TWLT-8, December 199
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