Human Language Technology: The Babel Fish

The essay describes some of the main problems which meet us when trying to process human language on a computer. The overall approach is to look at what we would need to do in order to be able to build a device with the same general functionality as Douglas Adams' Babel fish. That is, a device which can take utterances spoken in one language and instantly translate them into speech in some other language

Classifying Amharic News Text Using Self-Organizing Maps

The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field. The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it

SenToy and FantasyA: evaluating affective gaming

Gaming is a highly relevant application area for Intelligent Agents and Human Computer Interaction (HCI). Computer games bring us a full set of new gaming experiences where synthetic characters take on the main role.Using affective input in the interaction with a game and in particular with a character is a recent and fairly unexplored dimension. This video presents a study of a tangible interaction device for affective input and its use in a role-playing game where emotions are part of the game logic

Contract bridge as a micro-world for reasoning about communication agents

We argue that bidding in the game of Contract Bridge can profitably be regarded as a micro-world suitable for experimenting with pragmatics. We sketch an analysis in which a "bidding system" is treated as the semantics of an artificial language, and show how this "language", despite its apparent simplicity, is capable of supporting a wide variety of common speech acts parallel to those in natural languages; we also argue that the reason for the relatively unsuccessful nature of previous attempts to write strong Bridge playing programs has been their failure to address the need to reason explicitly about knowledge, pragmatics, probabilities and plans. We give an overview of Pragma, a system currently under development at SICS, which embodies these ideas in concrete form, using a combination of rule-based inference, stochastic simulation, and "neural-net" learning. Examples are given illustrating the functionality of the system in its current form

Robust semantic analysis for adaptive speech interfaces

The DUMAS project develops speech-based applications that are adaptable to different users and domains. The paper describes the project's robust semantic analysis strategy, used both in the generic framework for the development of multilingual speech-based dialogue systems which is the main project goal, and in the initial test application, a mobile phone-based e-mail interface

Active Learning for Dialogue Act Classification

Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result

Natural Language Processing at the School of Information Studies for Africa

The lack of persons trained in computational linguistic methods is a severe obstacle to making the Internet and computers accessible to people all over the world in their own languages. The paper discusses the experiences of designing and teaching an introductory course in Natural Language Processing to graduate computer science students at Addis Ababa University, Ethiopia, in order to initiate the education of computational linguists in the Horn of Africa region

Multi-session group scenarios for speech interface design

When developing adaptive speech-based multilingual interaction systems, we need representative data on the user's behaviour. In this paper we focus on a data collection method pertaining to adaptation in the user's interaction with the system. We describe a multi-session group scenario for Wizard of Oz studies with two novel features: firstly, instead of doing solo sessions with a static mailbox, our test users communicated with each other in a group of six, and secondly, the communication took place over several sessions in a period of five to eight days. The paper discusses our data collection studies using the method, concentrating on the usefulness of the method in terms of naturalness of the interaction and long-term developments

Methods for Amharic part-of-speech tagging

The paper describes a set of experiments involving the application of three state-of- the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for Eng- lish, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy ap- proach, while HMM-based and SVM- based taggers got comparable results

A mobile fitness companion

The paper introduces a Mobile Companion prototype, which helps users to plan and keep track of their exercise activities via an interface based mainly on speech input and output. The Mobile Companion runs on a PDA and is based on a stand-alone, speaker-independent solution, making it fairly unique among mobile spoken dialogue systems, where the common solution is to run the ASR on a separate server or to restrict the speech input to some specific set of users. The prototype uses a GPS receiver to collect position, distance and speed data while the user is exercising, and allows the data to be compared to previous exercises. It communicates over the mobile network with a stationary system, placed in the user’s home. This allows plans for exercise activities to be downloaded from the stationary to the mobile system, and exercise result data to be uploaded once an exercise has been completed