Using supertags as source language context in SMT

Recent research has shown that Phrase-Based Statistical Machine Translation (PB-SMT) systems can benefit from two enhancements: (i) using words and POS tags as context-informed features on the source side; and (ii) incorporating lexical syntactic descriptions in the form of supertags on the target side. In this work we present a novel PB-SMT model that combines these two aspects by using supertags as source language contextinformed features. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar. We use a memory-based classification framework that enables the estimation of these features while avoiding problems of sparseness. Despite the differences between these two approaches, the supertaggers give similar improvements. We evaluate the performance of our approach on an English-to-Chinese translation task using a state-of-the-art phrase-based SMT system, and report an improvement of 7.88% BLEU score in translation quality when adding supertags as context-informed features

Graph Subsumption in Abstract State Space Exploration

In this paper we present the extension of an existing method for abstract graph-based state space exploration, called neighbourhood abstraction, with a reduction technique based on subsumption. Basically, one abstract state subsumes another when it covers more concrete states; in such a case, the subsumed state need not be included in the state space, thus giving a reduction. We explain the theory and especially also report on a number of experiments, which show that subsumption indeed drastically reduces both the state space and the resources (time and memory) needed to compute it.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611

Scene extraction in motion pictures

This paper addresses the challenge of bridging the semantic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today\u27s content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the problem of automatic decomposition of motion pictures into meaningful story units, namely scenes. Since a scene is a complicated and subjective concept, we first propose guidelines from fill production to determine when a scene change occurs. We then investigate different rules and conventions followed as part of Fill Grammar that would guide and shape an algorithmic solution for determining a scene. Two different techniques using intershot analysis are proposed as solutions in this paper. In addition, we present different refinement mechanisms, such as film-punctuation detection founded on Film Grammar, to further improve the results. These refinement techniques demonstrate significant improvements in overall performance. Furthermore, we analyze errors in the context of film-production techniques, which offer useful insights into the limitations of our method

University of Sheffield TREC-8 Q & A System

The system entered by the University of Sheffield in the question answering track of TREC-8 is the result of coupling two existing technologies - information retrieval (IR) and information extraction (IE). In essence the approach is this: the IR system treats the question as a query and returns a set of top ranked documents or passages; the IE system uses NLP techniques to parse the question, analyse the top ranked documents or passages returned by the IR system, and instantiate a query variable in the semantic representation of the question against the semantic representation of the analysed documents or passages. Thus, while the IE system by no means attempts “full text understanding", this approach is a relatively deep approach which attempts to work with meaning representations. Since the information retrieval systems we used were not our own (AT&T and UMass) and were used more or less “off the shelf", this paper concentrates on describing the modifications made to our existing information extraction system to allow it to participate in the Q & A task