Today there are many services which provide information over the phone using
a prerecorded or synthesized voice. These voices are invariant in speed. Humans giving information over the telephone, however, tend to adapt the speed of their presentation to suit the needs of the listener. This paper presents a preliminary model of this adaptation. In a corpus of simulated directory assistance dialogs the operator’s speed in number-giving correlates with the speed of the user’s initial response and with the user’s speaking rate.
Designing user interfaces nowadays is indispensably important. A well-designed user interface promotes users to complete their everyday tasks in a great extent, particularly users with special needs. Numerous guidelines have already been developed for designing user interfaces but because of the technical development, new challenges appear continuously, various ways of information seeking, publication and transmit evolve.
The GDA (Global Document Annotation) project proposes a tag set which allows machines to automatically infer the underlying semantic/pragmatic structure of documents. Its objectives are to promote development and spread of N L P / A I applications to render GDA-tagged documents versatile and intelligent contents, which should nmtivate W W W (World Wide Web) users to tag their documents as part of content authoring.
The dialogue strategies used by a spoken dialogue system strongly influence performance and user satisfaction. An ideal system would not use a single fixed strategy, but would adapt to the circumstances at hand. To do so, a system must be able to identify dialogue properties that suggest adaptation. This paper focuses on identifying situations where the speech recognizer is performing poorly. We adopt a machine learning approach to learn rules from a dialogue corpus for identifying these situations. ...
We present ConsentCanvas, a system which structures and “texturizes” End-User License Agreement (EULA) documents to be more readable. The system aims to help users better understand the terms under which they are providing their informed consent. ConsentCanvas receives unstructured text documents as input and uses unsupervised natural language processing methods to embellish the source document using a linked stylesheet.
To enable conversational QA, it is important to examine key issues addressed in conversational systems in the context of question answering. In conversational systems, understanding user intent is critical to the success of interaction. Recent studies have also shown that the capability to automatically identify problematic situations during interaction can signiﬁcantly improve the system performance. Therefore, this paper investigates the new implications of user intent and problematic situations in the context of question answering. ...
We propose a plan-based approach for responding to user queries in a collaborative environment. We argue that in such an environment, the system should not accept the user's query automatically, but should consider it a proposal open for negotiation. In this paper we concentrate on cases in which the system and user disagree, and discuss how this disagreement can be detected, negotiated, and how final modifications should be made to the existing plan.
Kamp's Discourse Representation T h e o r y is a major breakthrough regarding the systematic translation of natural language discourse into logical form. W e have therefore chosen to m a r r y the User Specialty Languages System, which was originally designed as a natural language frontend to a relational database system, with this n e w theory. In the paper w e try to s h o w taking - for the sake of simplicity - Kemp's fragment of English h o w this is achieved.
Document revision histories are a useful and abundant source of data for natural language processing, but selecting relevant data for the task at hand is not trivial. In this paper we introduce a scalable approach for automatically distinguishing between factual and ﬂuency edits in document revision histories.
This paper presents the ﬁrst demonstration of a statistical spoken dialogue system that uses automatic belief compression to reason over complex user goal sets. Reasoning over the power set of possible user goals allows complex sets of user goals to be represented, which leads to more natural dialogues. The use of the power set results in a massive expansion in the number of belief states maintained by the Partially Observable Markov Decision Process (POMDP) spoken dialogue manager.
In information retrieval, genre classification could enable users to sort search results according to their immediate interests. People who go into a bookstore or library are not usually looking simply for information about a particular topic, but rather have requirements of genre as well: they are looking for scholarly articles about hypnotism, novels about the French Revolution, editorials about the supercollider, and so forth.
The adoption of Machine Translation technology for commercial applications is hampered by the lack of trust associated with machine-translated output. In this paper, we describe TrustRank, an MT system enhanced with a capability to rank the quality of translation outputs from good to bad. This enables the user to set a quality threshold, granting the user control over the quality of the translations. We quantify the gains we obtain in translation quality, and show that our solution works on a wide variety of domains and language pairs. ...
We use an EM algorithm to learn user models in a spoken dialog system. Our method requires automatically transcribed (with ASR) dialog corpora, plus a model of transcription errors, but does not otherwise need any manual transcription effort. We tested our method on a voice-controlled telephone directory application, and show that our learned models better replicate the true distribution of user actions than those trained by simpler methods and are very similar to user models estimated from manually transcribed dialogs. ...
This paper describes a novel approach for the automatic generation and evaluation of a trivial dialogue phrases database. A trivial dialogue phrase is deﬁned as an expression used by a chatbot program as the answer of a user input. A transfer-like genetic algorithm (GA) method is used to generating the trivial dialogue phrases for the creation of a natural language generation (NLG) knowledge base. The automatic evaluation of a generated phrase is performed by producing n-grams and retrieving their frequencies from the World Wide Web (WWW).
Assessing the quality of user generated content is an important problem for many web forums. While quality is currently assessed manually, we propose an algorithm to assess the quality of forum posts automatically and test it on data provided by Nabble.com. We use state-of-the-art classiﬁcation techniques and experiment with ﬁve feature classes: Surface, Lexical, Syntactic, Forum speciﬁc and Similarity features. We achieve an accuracy of 89% on the task of automatically assessing post quality in the software domain using forum speciﬁc features.
We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. Unlike previous studies that focus on user’s knowledge or typical kinds of users, the user model we propose is more comprehensive. Speciﬁcally, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree of hastiness. Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. We obtained reasonable classiﬁcation accuracy for all dimensions.
Machine translation (MT) systems have improved signiﬁcantly; however, their outputs often contain too many errors to communicate the intended meaning to their users. This paper describes a collaborative approach for mediating between an MT system and users who do not understand the source language and thus cannot easily detect translation mistakes on their own. Through a visualization of multiple linguistic resources, this approach enables the users to correct difﬁcult translation errors and understand translated passages that were otherwise bafﬂing. ...
We use a machine learner trained on a combination of acoustic and contextual features to predict the accuracy of incoming n-best automatic speech recognition (ASR) hypotheses to a spoken dialogue system (SDS). Our novel approach is to use a simple statistical User Simulation (US) for this task, which measures the likelihood that the user would say each hypothesis in the current context. Such US models are now common in machine learning approaches to SDS, are trained on real dialogue data, and are related to theories of “alignment” in psycholinguistics.
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The gSOAP tools provide a SOAP/XML-to-C/C++ language binding to ease the development
of SOAP/XML Web services and client application in C and C++. Most toolkits for C++ Web
services adopt a SOAP-centric view and oﬀer APIs that require the use of class libraries for SOAP-
speciﬁc data structures. This often forces a user to adapt the application logic to these libraries. In
contrast, gSOAP provides a C/C++ transparent SOAP API through the use of compiler technology
that hides irrelevant SOAP-speciﬁc details from the user.