Axiomatic analysis of translation language model for information retrieval

Statistical translation models have been shown to outperform simple document language models which rely on exact matching of words in the query and documents. A main challenge in applying translation models to ad hoc information retrieval is to estimate a translation model without training data. In this paper, we perform axiomatic analysis of translation language model for retrieval in order to gain insights about how to optimize the estimation of translation probabilities. We propose a set of constraints that a reasonable translation language model should satisfy. We check these constraints on the state-of-the-art translation estimation method based on Mutual Information and find that it does not satisfy most of the constraints. We then propose a new estimation method that better satisfies the defined constraints. Experimental results on representative TREC data sets show that the proposed new estimation method outperforms the existing Mutual Information-based estimation, suggesting that the proposed constraints are indeed helpful for designing better estimation methods for translation language model.