IBM models are very important word alignment models in Machine Translation. Following the Maximum Likelihood Estimation principle to estimate their parameters, the models will easily overfit the training data when the data are sparse. While smoothing is a very popular solution in Language Model, there still lacks studies on smoothing for word alignment. In this paper, we propose a framework which generalizes the notable work Moore [2004] of applying additive smoothing to word alignment models. The framework allows developers to customize the smoothing amount for each pair of word. The added amount will be scaled appropriately by a common factor which reflects how much the framework trusts the adding strategy according to the performance on data. We also carefully examine various performance criteria and propose a smoothened version of the error count, which generally gives the best result.
However, there is still lack of studies for applying smoothing techniques to the word alignment problem. The work of this paper is mostly an extended work of @cite_7 which is the earliest study for this matter. In that work, Moore has an intensive study of many improvements to IBM Model 1. Additive smoothing is the first of the three improvements, which directly attempts to solve the sparsity problem. In spite of the simplicity, the method has good performance. The smoothing technique Moore applied is additive smoothing. For every pair of word @math , it is assumed that @math and @math were paired in a constant number of times before the training data is observed. Therefore, in each estimation, this assumption will be taken into account and the prior number of times, which they are paired, will be added appropriately. The constant prior count is tuned manually or learnt from some additional annotated data.
@cite_7
2172138510 2158195707 2111305191 2110993209
We investigate a number of simple methods for improving the word-alignment accuracy of IBM Model 1. We demonstrate reduction in alignment error rate of approximately 30 resulting from (1) giving extra weight to the probability of alignment to the null word, (2) smoothing probability estimates for rare words, and (3) using a simple heuristic estimation method to initialize, or replace, EM training of model parameters. We survey the most widely-used algorithms for smoothing models for language n -gram modeling. We then present an extensive empirical comparison of several of these smoothing techniques, including those described by Jelinek and Mercer (1980); Katz (1987); Bell, Cleary and Witten (1990); Ney, Essen and Kneser (1994), and Kneser and Ney (1995). We investigate how factors such as training data size, training corpus (e.g. Brown vs. Wall Street Journal), count cutoffs, and n -gram order (bigram vs. trigram) affect the relative performance of these methods, which is measured through the cross-entropy of test data. We find that these factors can significantly affect the relative performance of models, with the most significant factor being training data size. Since no previous comparisons have examined these factors systematically, this is the first thorough characterization of the relative performance of various algorithms. In addition, we introduce methodologies for analyzing smoothing algorithm efficacy in detail, and using these techniques we motivate a novel variation of Kneser?Ney smoothing that consistently outperforms all other algorithms evaluated. Finally, results showing that improved language model smoothing leads to improved speech recognition performance are presented. In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n -grams, skipping, interpolated Kneser?Ney smoothing, and clustering. We present explorations of variations on, or of the limits of, each of these techniques, including showing that sentence mixture models may have more potential. While all of these techniques have been studied separately, they have rarely been studied in combination. We compare a combination of all techniques together to a Katz smoothed trigram model with no count cutoffs. We achieve perplexity reductions between 38 and 50 (1 bit of entropy), depending on training data size, as well as a word error rate reduction of 8.9 . Our perplexity reductions are perhaps the highest reported compared to a fair baseline. Training corpora for stochastic language models are virtually always too small for maximum-likelihood estimation, so smoothing the models is of great importance. The authors derive the cooccurrence smoothing technique for stochastic language modeling and give experimental evidence for its validity. Using word-bigram language models, cooccurrence smoothing improved the test-set perplexity by 14 on a German 100000-word text corpus and by 10 on an English 1-million word corpus. >