Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.Comment: 21 pages, 2 figures, 10 table

A Modular Logic Approach for Expressing Web Services in XML Applying Dynamic Rules in XML

RuleML is considered to be a markup language for the semantic web. It allows the enrichment of web ontologies by adding definitions of derived concepts and it enhances interoperability among different systems and tools by publishing rules in an XML format. Moreover the in-creasing demand for interfaces that enhance information sharing has given rise to XML doc-uments that include embedded calls to web services. In this paper we propose a variation of RuleML that is based on modular logic programming. Our approach is based in a two level architecture. In the first level a modular logic language, called M-log, is presented. This lan-guage encompasses several mechanisms for invoking web services. In the second level we ex-ploit the semantics of M-log to present a variation of RuleML with rich modeling capabilities. Formal foundations for this variation are given through direct translation to M-log semantics.Knowledge Management, XML, Modular Logic Programming, E-Services

Geometry of abstraction in quantum computation

Quantum algorithms are sequences of abstract operations, performed on non-existent computers. They are in obvious need of categorical semantics. We present some steps in this direction, following earlier contributions of Abramsky, Coecke and Selinger. In particular, we analyze function abstraction in quantum computation, which turns out to characterize its classical interfaces. Some quantum algorithms provide feasible solutions of important hard problems, such as factoring and discrete log (which are the building blocks of modern cryptography). It is of a great practical interest to precisely characterize the computational resources needed to execute such quantum algorithms. There are many ideas how to build a quantum computer. Can we prove some necessary conditions? Categorical semantics help with such questions. We show how to implement an important family of quantum algorithms using just abelian groups and relations.Comment: 29 pages, 42 figures; Clifford Lectures 2008 (main speaker Samson Abramsky); this version fixes a pstricks problem in a diagra