Dialogue Act Recognition via CRF-Attentive Structured Network

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.Comment: 10 pages, 4figure

The Centre for Speech, Language and the Brain (CSLB) concept property norms.

Theories of the representation and processing of concepts have been greatly enhanced by models based on information available in semantic property norms. This information relates both to the identity of the features produced in the norms and to their statistical properties. In this article, we introduce a new and large set of property norms that are designed to be a more flexible tool to meet the demands of many different disciplines interested in conceptual knowledge representation, from cognitive psychology to computational linguistics. As well as providing all features listed by 2 or more participants, we also show the considerable linguistic variation that underlies each normalized feature label and the number of participants who generated each variant. Our norms are highly comparable with the largest extant set (McRae, Cree, Seidenberg, & McNorgan, 2005) in terms of the number and distribution of features. In addition, we show how the norms give rise to a coherent category structure. We provide these norms in the hope that the greater detail available in the Centre for Speech, Language and the Brain norms should further promote the development of models of conceptual knowledge. The norms can be downloaded at www.csl.psychol.cam.ac.uk/propertynorms

Talking about trees, scope and concepts

Cimiano P, Reyle U. Talking about trees, scope and concepts. In: Bunt H, Geertzen J, Thijse E, eds. Proceedings of the 6th Workshop on Computational Semantics (IWCS). 2005

Problems with evaluation of unsupervised empirical grammatical inference systems

Abstract. Empirical grammatical inference systems are practical systems that learn structure from sequences, in contrast to theoretical grammatical inference systems, which prove learnability of certain classes of grammars. All current empirical grammatical inference evaluation methods are problematic, i.e. dependency on language experts, appropriateness and quality of an underlying grammar of the data, and influence of the parameters of the evaluation metrics. Here, we propose a modification of an evaluation method to reduce the ambiguity of results

Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing.

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature-based conceptual account assumes that the statistical characteristics of concepts' features--the number of concepts they occur in (distinctiveness/sharedness) and likelihood of co-occurrence (correlational strength)--determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech-to-meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time-sensitive co-occurrence-driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general-to-specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation.This work was supported by a European Research Council Advanced Investigator grant (under the European Community's Seventh Framework Programme (FP7/2007-2013/ ERC Grant agreement no 249640) to LKT, and a Marie Curie Intra-European Fellowship and Swiss National Science Foundation Ambizione Fellowship to KIT. We thank Ken McRae and colleagues for making their property norm data available. We are very grateful to George Cree and Chris McNorgan for providing us with the MikeNet implementation of their model.This is the final published version. It first appeared at http://dx.doi.org/10.1111/cogs.1223

The informativeness of linguistic unit boundaries

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Pacini Editore