A grammatical specification of human-computer dialogue

The Seeheim Model of human-computer interaction partitions an interactive application into a user-interface, a dialogue controller and the application itself. One of the formal techniques of implementing the dialogue controller is based on context-free grammars and automata. In this work, we modify an off-the-shelf compiler generator (YACC) to generate the dialogue controller. The dialogue controller is then integrated into the popular X-window system, to create an interactive-application generator. The actions of the user drive the automaton, which in turn controls the application

A Stochastic Decoder for Neural Machine Translation

The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence. This gives rise to significant variation in parallel cor- pora, however, most current models of machine translation do not account for this variation, instead treating the prob- lem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to ac- count for local lexical and syntactic varia- tion in parallel corpora. We provide an in- depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on sev- eral different language pairs demonstrate that the model consistently improves over strong baselines.Comment: Accepted at ACL 201

SeLeCT: a lexical cohesion based news story segmentation system

In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. 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 approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between textual units using a linguistic technique called lexical chaining. We evaluate the relative performance of SeLeCT with respect to two other cohesion based segmenters: TextTiling and C99. Using a recently introduced evaluation metric WindowDiff, we contrast the segmentation accuracy of each system on both "spoken" (CNN news transcripts) and "written" (Reuters newswire) news story test sets extracted from the TDT1 corpus