Ravi and Knight, ACL 2011

Citation

S. Ravi and K. Knight. 2011. Deciphering Foreign Language. In Proceedings of ACL.

Online version

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Summary

This work addresses the Machine Translation problem without resorting to parallel training data.

This is done by looking at the Machine Translation task from the decipherment perspective, where a sentence in the source language is viewed as the sentence target, but encoded in some unknown symbols.

Experimental showed that, while the results using monolingual data were considerably lower than those using bilingual data if the same amount of data is used, large amounts of monolingual data can be used to create models that perform similarly to systems that use smaller amounts of bilingual data. This is encouraging, since bilingual data is a scarce resource for most language pairs and domains, while monolingual data is much more abundant.

Description of the Method

Word Alignments using parallel corpora is viewed as a maximization problem with latent word alignments $a$ for a set of sentence pairs $(s,t)$, given by:

$argmax_{\theta }\prod _{s,t}P_{\theta }(s|t)=argmax_{\theta }\prod _{s,t}\sum _{a}P_{\theta }(s,a|t)$

where $\theta$ are the translation parameters of the model.

When only monolingual corpora is used, for each source sentence $s$, there isn't an exact target sentence that is aligned to the source sentence. Thus, like the word alignments, this work views the hidden target sentence as an additional latent variable. Hence, the previous equation can be rewritten as:

$argmax_{\theta }\prod _{s}\sum _{t}P(t)\sum _{a}P_{\theta }(s,a|t)$

where P(t) is the probability of a target sentence $t$, modeled by the a language model. The large number of possible latent variable that is generated from this model is tacked using Gibbs sampling.

As for the translation model $P_{\theta }(s,a|t)$, two models are presented.

The first model is a simple model that accounts for word substitutions, insertions, deletions and relative distortion, but does not incorporate word fertility and absolute distortion as in IBM Model 3. The decision to leave out the word fertility and absolute distortion is due to the fact that EM training would be intractable due to the large sizes for the fertility and distortion parameter tables and the resulting derivation lattices.

The second model accounts for the same issues addressed in IBM Model 3 and a Bayesian method is used instead of EM (which is intractable). In this method, model parameters are learned using a Chinese Restaurant Process, rather than using expected counts. For instance, the translation parameters $t_{\theta }(s_{i}|t_{i})$, which is generally calculated as the ratio between the number of expected observations of $s_{i}$ and $t_{i}$, $C_{e}(t_{i},s_{i})$, and the number of observations of $t_{i}$, $C_{e}(t_{i})$, is now formulated as:

$t_{\theta }(s_{i}|t_{i})={\frac {\alpha P_{0}(s_{i}|t_{i})+C_{history}(t_{i},s_{i})}{\alpha +C_{history}(t_{i})}}$

where $P_{0}(s_{i}|t_{i})$ is a base distribution (which is set to uniform) and $C_{history}$ represents the count events occurring in the history. Finally, a Dirichlet prior is applied, which is parametrized by $\alpha$.