Question retrieval in current community-based question answering (CQA) services does not, in general, work well for long and complex queries. One of the main difficulties lies in the word mismatch between queries and candidate questions. Existing solutions try to expand the queries at word level, but they usually fail to consider concept level enrichment. In this paper, we explore a pivot language translation based approach to derive the paraphrases of key concepts. We further propose a unified question retrieval model which integrates the keyconcepts and their paraphrases for the query question. Experimental results demonstrate that the paraphrase enhanced retrieval model significantly outperforms the state-of-the-art models in question retrieval.

This paper proposes a method for the development of AI for autonomous agents in game worlds modeled by fixed regular grids.  This approach uses context behaviors driven by distance maps to support multiple agents in a dynamic environment.  This paper introduces the notion of hierarchical distance maps which allow for higher-level goals to be easily specified by designers.  We also discuss potential applications of our approach in the design and development of agents and behaviors in the block world genre.