Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient. This paper provides a novel estimator to speed up the learning based on Noise Contrastive Estimate, extended for documents of variant lengths and weighted inputs. Experiments on two benchmarks show that the new estimator achieves great learning efficiency and high accuracy on document retrieval and classification.

Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships that are unlikely to last and examine temporal patterns that foretell betrayal. We reveal that subtle signs of imminent betrayal are encoded in the conversational patterns of the dyad, even if the victim is not aware of the relationship's fate. In particular, we find that lasting friendships exhibit a form of balance that manifests itself through language. In contrast, sudden changes in the balance of certain conversational attributes---such as positive sentiment, politeness, or focus on future planning---signal impending betrayal.

In this paper, we develop the continuous time dynamic topic model (cDTM). The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequential collection of documents, where a "topic" is a pattern of word use that we expect to evolve over the course of the collection. We derive an efficient variational approximate inference algorithm that takes advantage of the sparsity of observations in text, a property that lets us easily handle many time points. In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized. Moreover, the complexity of variational inference for the dDTM grows quickly as time granularity increases, a drawback which limits fine-grained discretization. We demonstrate the cDTM on two news corpora, reporting both predictive perplexity and the novel task of time stamp prediction.

Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known in advance, and this is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model in this paper. Instead of using fixed-dimensional probability distributions in its generative model, we use stochastic processes. Specifically, a gamma process is assigned to each document, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different gamma process from the global gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics.

Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditional text mining models has gained significant interests in the area of information retrieval, statistical natural language processing, and machine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) model to resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determine the number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in order to capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma process that accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm with each conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets show the capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topics simultaneously.

Development of multimodal applications is an iterative, complex, and often a rather heuristic process. This is because in multimodal systems the number of interplaying components can be far greater than in an unimodal Spoken Dialogue System. From the developer's perspective, a multimodal system presents challenges and technical difficulties on many levels. In this paper we will describe our approach to one specific component of multimodal systems, the Multimodal Integrator. On the other hand, from the designer's perspective, all components must be fine-tuned to a level that their combined overall performance can deliver the desired experience to end users. In both cases, evaluation and analysis of the current implementation is paramount. Hence, looking into the details while getting a good understanding of the overall performance of a multimodal system is the other key topic.

We describe TickTock, a conversational agent designed to engage humans on topics of its choosing and to carry on an interaction for as long as possible. Our prototype uses a database of talk show transcripts featuring guests from the film industry. To be an interesting companion Tick Tock uses immediate context from the last two turns to formulate queries into a database of utterances. The process is automatic. TickTock monitors user engagement and performs certain moves, such as topic shifts, based on its assessment of user state. Initially we used utterance content for monitoring and subsequently we begun to investigate non-language cues, such as prosody and visual cues to create a more robust engagement model based on multiple human communication channels.

Systems capable of highly-interactive dialog have recently been developed in several domains. This paper considers how to build on these successes to make systems more robust, easier to develop, more adaptable, and more scientifically significant.

One theory of turn-taking in dialogue is that the current speaker controls when the other conversant can speak, which is also the basis of most spoken dialogue systems. A second theory is that the two conversants negotiate who will speak next. In this paper, we testthese theories by examining how well an overhearer can predict this,based only on the current speaker's utterance, which is what the other conversant would have access to. We had overhearers listen to the current speaker and indicate whether they felt the current speaker will continue or not. Our results support the negotiative model.

In this work, we make a contribution to developing turn-taking mechanism in spoken dialogue systems. We focus on modelling the turn-taking behavior in human-human conversations. The proposed models are tested on the Switchboard corpus which contains conversations annotated at the utterance level. Several experiments were performed to analyze the salience of different features that are associated with the preceding utterances for the task of predicting whether there will be a change in speaker. The impact of the n-gram sequential modelling on turn-taking is studied. Machine learning techniques are also employed to perform this prediction task. Results from the experiments suggest that a combination of the preceding dialogue sequence, previous changes in speaker information and duplicating the sequences by replacing speaker IDs plays an important role in modelling turn-taking. Utterance sequences of length 3 in N-grams resulted in higher predictability for this task. Experiments suggest that a machine learning technique with 4-grams of a combination of all these features is effective for predicting speaker changes.