A process model based methodology to support conflict management

The product design development has increasingly become a collaborative process. Conflicts often appear in the design process due to multi-actors interactions. Therefore, a critical element of collaborative design would be conflict situations resolution. In this paper, a methodology, based on a process model, is proposed to support conflict management. This methodology deals mainly with the conflict resolution team identification and the solution impact evaluation issues. The proposed process model allows the design process traceability and the data dependencies network identification; which making it be possible to identify the conflict resolution actors as well as to evaluate the selected solution impact. Copyright © 2006 IFAC.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.