Improving Search through A3C Reinforcement Learning based Conversational Agent

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.Comment: 17 pages, 7 figure

Segmenting broadcast news streams using lexical chains

In this paper we propose a course-grained NLP approach to text segmentation based on the analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling

Confluence of Vision and Natural Language Processing for Cross-media Semantic Relations Extraction

In this dissertation, we focus on extracting and understanding semantically meaningful relationships between data items of various modalities; especially relations between images and natural language. We explore the ideas and techniques to integrate such cross-media semantic relations for machine understanding of large heterogeneous datasets, made available through the expansion of the World Wide Web. The datasets collected from social media websites, news media outlets and blogging platforms usually contain multiple modalities of data. Intelligent systems are needed to automatically make sense out of these datasets and present them in such a way that humans can find the relevant pieces of information or get a summary of the available material. Such systems have to process multiple modalities of data such as images, text, linguistic features, and structured data in reference to each other. For example, image and video search and retrieval engines are required to understand the relations between visual and textual data so that they can provide relevant answers in the form of images and videos to the users\u27 queries presented in the form of text. We emphasize the automatic extraction of semantic topics or concepts from the data available in any form such as images, free-flowing text or metadata. These semantic concepts/topics become the basis of semantic relations across heterogeneous data types, e.g., visual and textual data. A classic problem involving image-text relations is the automatic generation of textual descriptions of images. This problem is the main focus of our work. In many cases, large amount of text is associated with images. Deep exploration of linguistic features of such text is required to fully utilize the semantic information encoded in it. A news dataset involving images and news articles is an example of this scenario. We devise frameworks for automatic news image description generation based on the semantic relations of images, as well as semantic understanding of linguistic features of the news articles

Absence of posture-dependent and posture-congruent memory effects on the recall of action sentences

[EN]In two experiments with large samples of participants, we explored contextual memory effects associated with body posture, which was considered a physical and proprioceptive context and, therefore, potentially relevant to the encoding and retrieval of information. In Experiment 1 (N = 128), we studied the effect of context dependence on memory by manip ulating the body posture adopted by the participants during the incidental encoding and sub sequent recall of a series of action sentences not intrinsically associated with particular body postures (e.g., “to put on a pair of glasses”, “to look at a postcard”). Memory perfor mance was not affected by context manipulation, as reflected by the absence of significant differences between remembering while in the posture adopted at study or in a different pos ture. Experiment 2 (N = 85) was designed to analyze context congruency memory effects, and for that purpose we manipulated the participants’ body posture during the recall of sen tences that described actions usually performed in body postures that were congruent or incongruent with the posture of the participants (e.g., recalling the sentence “to travel by taxi” while sitting or while standing). A content-neutral posture (lying) was used for the inci dental encoding phase. Memory performance was not affected by contextual congruency at the time of recall, as evidenced by the lack of significant differences between recalling in a posture congruent with the content to be recalled and recalling in an alternative posture. Bayesian analyses supported the strength of null findings in the two experiments, adding to the evidence that, when taken together, the results in this study clearly failed to show con textual memory effects of body posture on the recall of action-related verbal statements

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Verbs as linguistic markers of agency: The social side of grammar

open4Basic grammatical categories may carry social meanings irrespective of their semantic content. In a set of four studies, we demonstrate that verbs—a basic linguistic category present and distinguishable in most languages—are related to the perception of agency, a fundamental dimension of social perception. In an archival analysis of actual language use in Polish and German, we found that targets stereotypically associated with high agency (men and young people) are presented in the immediate neighborhood of a verb more often than non-agentic social targets (women and older people). Moreover, in three experiments using a pseudo-word paradigm, verbs (but not adjectives and nouns) were consistently associated with agency (but not with communion). These results provide consistent evidence that verbs, as grammatical vehicles of action, are linguistic markers of agency. In demonstrating meta-semantic effects of language, these studies corroborate the view of language as a social tool and an integral part of social perception.openFormanowicz, Magdalena; Roessel, Janin; Suitner, Caterina; Maass, AnneFormanowicz, Magdalena; Roessel, Janin; Suitner, Caterina; Maass, Ann