A Machine Learning Approach to the Classification of Dialogue Utterances

The purpose of this paper is to present a method for automatic classification of dialogue utterances and the results of applying that method to a corpus. Superficial features of a set of training utterances (which we will call cues) are taken as the basis for finding relevant utterance classes and for extracting rules for assigning these classes to new utterances. Each cue is assumed to partially contribute to the communicative function of an utterance. Instead of relying on subjective judgments for the tasks of finding classes and rules, we opt for using machine learning techniques to guarantee objectivity.Comment: 12 pages, using nemlap.sty, harvard.sty and agsm.bst, to appear in Proceedings of NeMLaP-2, Bilkent University, Ankara, Turke

Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

This paper presents the outcomes from a cognitive engineering project addressing the design problems of computerized monitoring in neonatal intensive care. Cognitive engineering is viewed, in this project, as a symbiosis between cognitive science and design practice. A range of methodologies has been used: interviews with neonatal staff, ward observations and experimental techniques. The results of these investigations are reported, focusing specifically on the differences between junior and senior physicians in their interpretation of monitored physiological data. It was found that the senior doctors made better use of the different knowledge sources available than the junior doctors. The senior doctors were able to identify more relevant physiological patterns and generated more and better inferences than did their junior colleagues. Expertise differences are discussed in the context of previous psychological research in medical expertise. Finally, the paper discusses the potential utility of these outcomes to inform the design of computerized decision support in neonatal intensive care

Understanding Hidden Memories of Recurrent Neural Networks

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2017

Competing creole transcripts on trial

A criminal prosecution of Jamaican Creole (JC) speaking ‘posse’(=gang) members in New York included evidence of recorded speech in JC. Clandestinerecordings (discussions of criminal events, including narration of a homicide) wereintroduced at trial. Taped data were translated for prosecution by a non-linguist nativespeaker of JC. Defense disputed these texts and commissioned alternative transcriptionsfrom a creolist linguist, who was a non-speaker of JC. Prosecution in turn hired anothercreolist, a near-native speaker of and specialist in JC, to testify on the relative accuracyof both sets of earlier texts. Differing representations of key conversations weresubmitted to a non-creole speaking judge/jury, both linguists testified, and defendantswere convicted. The role of linguistic testimony and practice (especially transcription)in the trial is analysed. A typology of linguistic expertise is given, and effects of thelanguage’s Creole status and lack of instrumentalization on the trial are discussed

Interpretation, translation and intercultural communication in refugee status determination procedures in the UK and France

This article explores the interplay between language and intercultural communication within refugee status determination procedures in the UK and France, using material taken from ethnographic research that involved a combination of participant observation, semi-structured interviews and documentary analysis in both countries over a two-year period (2007–2009). It is concerned, in particular, to examine the role played by interpreters in facilitating intercultural communication between asylum applicants and the different administrative and legal actors responsible for assessing or defending their claims. The first section provides an overview of refugee status determination procedures in the UK and France, introducing the main administrative and legal contexts of the asylum process within which interpreters operate in the two countries. The second section compares the organisation of interpreting services, codes of conduct for interpreters and institutional expectations about the nature of interpreters’ activity on the part of the relevant UK and French authorities. The third section then explores some of the practical dilemmas for interpreters and barriers to communication that exist in refugee status determination procedures in the two countries. The article concludes by emphasising the complex and active nature of the interpreter's role in UK and French refugee status determination procedures

Assessing agreement on classification tasks: the kappa statistic

Currently, computational linguists and cognitive scientists working in the area of discourse and dialogue argue that their subjective judgments are reliable using several different statistics, none of which are easily interpretable or comparable to each other. Meanwhile, researchers in content analysis have already experienced the same difficulties and come up with a solution in the kappa statistic. We discuss what is wrong with reliability measures as they are currently used for discourse and dialogue work in computational linguistics and cognitive science, and argue that we would be better off as a field adopting techniques from content analysis.Comment: 9 page