When people interact with language-producing agents (other people or computers), they assume that the shared experience leads to shared representations — of the world, the interaction, and the language used in the interaction. This phenomenon occurs even during interaction with systems that give no evidence of building shared representations. The absence of shared representations leads to errors and delays; alternatively, even simple shared representations can lead to reduced error rates and more efficient interaction. In this talk, we present three case studies: a mobile local business search application that builds no interaction representations; a telephone-based recommendation and review system that builds limited representations of the shared language in the interaction; and computer models of coreference that use shared representations to permit both coreference resolution and referring expression generation. We lay out a range of possibilities for shared representations, show that they can be built incrementally as an interaction progresses, and point to possibilities for future work in probabilistic shared representations for interactive systems.

But each They are linked by an arrow which is labeled by discourse method for representing a context is quite different. Our relations R. We represent SDRS in the form of boxes like study is based on two representational methods of temporal DRS. To induce a temporal and hierarchical structure, relations: the Segmented Discourse Representation Theory SDRT distinguish discourse relations'coordinating' from (SDRT) and the model of Cognitive and Applicative'subordinating', therefore coordination and subordination Grammar (CAG). This paper presents a comparison of affect the temporal order of text: the former indicate a continuation these two approaches about aspect and tense by an analysis of some discourses pattern, like relations of'Narration' of relations between events. We are not going to show all or'Result' in discourse segmentation, and the later steps of SDRT's representations, but we take a simple discourse indicate with types of information like relations of'Elaboration' (Asher and Lascarides 2003) and we analyze the or'Explanation'. These relations are appeared same discourse with the framework of the CAG.

The aim of this paper is threefold: (1) it explores methods for the detection of affective states in text, (2) it presents the usage of such affective cues in a conversational system and (3) it evaluates its effectiveness in a virtual reality setting. Valence and arousal values, used for generating facial expressions of users' avatars, are also incorporated into the dialog, helping to bridge the gap between textual and visual modalities. The system is evaluated in terms of its ability to: (i) generate a realistic dialog, (ii) create an enjoyable chatting experience, and (iii) establish an emotional connection with participants. Results show that user ratings for the conversational agent match those obtained in a Wizard of Oz setting.

Spoken language is an important and natural way for people to communicate with computers. Nonetheless, habitable, reliable, and efficient human-machine dialogue remains difficult to achieve. This paper describes a multi-threaded semi-synchronous architecture for spoken dialogue systems. The focus here is on its utterance interpretation module. Unlike most architectures for spoken dialogue systems, this new one is designed to be robust to noisy speech recognition through earlier reliance on context, a mixture of rationales for interpretation, and fine-grained use of confidence measures. We report here on a pilot study that demonstrates its robust understanding of users’ objectives, and we compare it with our earlier spoken dialogue system implemented in a traditional pipeline architecture. Substantial improvements appear at all tested levels of recognizer performance.

Inferring the sentiment of social media content, for instance blog postings or online product reviews, is both of great interest to businesses and technically challenging to accomplish. This paper presents two computational methods for estimating social media sentiment which address the challenges associated with Web-based analysis. Each method formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and assumes that only limited prior information is available regarding the sentiment orientation of any of the documents or words of interest. The first algorithm is a semi-supervised sentiment classifier which combines knowledge of the sentiment labels for a few documents and words with information present in unlabeled data, which is abundant online. The second algorithm assumes existence of a set of labeled documents in a domain related to the domain of interest, and leverages these data to estimate sentiment in the target domain. We demonstrate the utility of the proposed methods by showing they outperform several standard methods for the task of inferring the sentiment of online reviews of movies, electronics products, and kitchen appliances. Additionally, we illustrate the potential of the methods for multilingual business informatics through a case study involving estimation of Indonesian public opinion regarding the July 2009 Jakarta hotel bombings.

We extend Latent Dirichlet Allocation (LDA) by explicitly allowing for the encoding of side information in the distribution over words. This results in a variety of new capabilities, such as improved estimates for infrequently occurring words, as well as the ability to leverage thesauri and dictionaries in order to boost topic cohesion within and across languages. We present experiments on multi-language topic synchronisation where dictionary information is used to bias corresponding words towards similar topics. Results indicate that our model substantially improves topic cohesion when compared to the standard LDA model.

We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Allocation (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collections, including those arriving in a stream. We study the performance of online LDA in several ways, including by fitting a 100-topic topic model to 3.3M articles from Wikipedia in a single pass. We demonstrate that online LDA finds topic models as good or better than those found with batch VB, and in a fraction of the time.

This paper presents a co-regularization based approach to semi-supervised domain adaptation. Our proposed approach (EA) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further assist the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement and can be applied as a pre-processing step to any supervised learner. Our theoretical analysis (in terms of Rademacher complexity) of EA and EA show that the hypothesis class of EA has lower complexity (compared to EA) and hence results in tighter generalization bounds. Experimental results on sentiment analysis tasks reinforce our theoretical findings and demonstrate the efficacy of the proposed method when compared to EA as well as few other representative baseline approaches.

While traditional research on text clustering has largely focused on grouping documents by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the author's mood, gender, age, or sentiment. Without knowing the user's intention, a clustering algorithm will only group documents along the most prominent dimension, which may not be the one the user desires. To address the problem of clustering documents along the user-desired dimension, previous work has focused on learning a similarity metric from data manually annotated with the user's intention or having a human construct a feature space in an interactive manner during the clustering process. With the goal of reducing reliance on human knowledge for fine-tuning the similarity function or selecting the relevant features required by these approaches, we propose a novel active clustering algorithm, which allows a user to easily select the dimension along which she wants to cluster the documents by inspecting only a small number of words. We demonstrate the viability of our algorithm on a variety of commonly-used sentiment datasets.

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as 'Place the cup down (on the table)," potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated using spoken language to manipulate objects.