Creation of a New Domain and Evaluation of Comparison Generation in a Natural Language Generation System

We describe the creation of a new domain for the Methodius Natural Language Generation System, and an evaluation of Methodius ’ parameterized comparison generation algorithm. The new domain was based around music and performers, and texts about the domain were generated using Methodius. Our evaluation showed that test subjects learned more from texts that contained comparisons than from those that did not. We also established that the comparison generation algorithm could generalize to the music domain.

Choosing the best comparison under the circumstances

Abstract. The Methodius Natural Language Generation systems generates personalized descriptions of objects from a collection. As part of the user modeling component, it creates comparisons between the current object being viewed and previous objects from the user history. We present our general algorithm for choosing the best comparison, which can be optimized to give the best result for different domains through a parameterizable scoring function.

Beetle II: an adaptable tutorial dialogue system

We present BEETLE II, a tutorial dialogue system which accepts unrestricted language input and supports experimentation with different dialogue strategies. Our first system evaluation compared two dialogue policies. The resulting corpus was used to study the impact of different tutoring and error recovery strategies on user satisfaction and student interaction style. It can also be used in the future to study a wide range of research issues in dialogue systems.

Multi-lingual Evaluation of a Natural Language Generation System

This paper describes a user evaluation of the text output from the M-PIRO (Multilingual Personalised Information Objects) system, which dynamically generates descriptions of exhibits for a virtual museum. We show that subjects performed significantly better in a factual recall test when the descriptions included more sophisticated text structuring modules. The subjects also judged the structured texts to be more interesting and readable, and felt that they had learned more from them. 1

Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences

When humans speak they often use gram-matically incorrect sentences, which is a problem for grammar-based language pro-cessing methods, since they expect in-put that is valid for the grammar. We present two methods to transform spoken language into grammatically correct sen-tences. The first is an algorithm for au-tomatic ellipsis detection, which finds el-lipses in spoken sentences and searches in a combinatory categorial grammar for suitable words to fill the ellipses. The sec-ond method is an algorithm that computes the semantic similarity of two words us-ing WordNet, which we use to find alter-natives to words that are unknown to the grammar. In an evaluation, we show that the usage of these two methods leads to an increase of 38.64 % more parseable sen-tences on a test set of spoken sentences that were collected during a human-robot interaction experiment.