| Introduction | Thanks to the combination of several resources, it is possible to obtain monolingual parallel corpora which are large enough to train domain-independent translation models. |
| Parallel Datasets | Table 1 gives some examples of word-to-word translations obtained for the different parallel corpora used (the column ALLp001 will be described in the next section). |
| Parallel Datasets | concatenating the parallel corpora , before training. |
| Related Work | These models attempt to address synonymy and polysemy problems by encoding statistical word associations trained on monolingual parallel corpora . |
| Abstract | We investigate the task of unsupervised constituency parsing from bilingual parallel corpora . |
| Abstract | Applying this model to three parallel corpora (Korean-English, Urdu-English, and Chinese-English) we find substantial performance gains over the CCM model, a strong monolingual baseline. |
| Model | We propose an unsupervised Bayesian model for learning bilingual syntactic structure using parallel corpora . |