| Collaborative Decoding | Model training . |
| Collaborative Decoding | 2.5 Model Training |
| Collaborative Decoding | Model training for co-decoding |
| Experiments | The language model used for all models (include decoding models and system combination models described in Section 2.6) is a 5-gram model trained with the English part of bilingual data and xinhua portion of LDC English Giga-word corpus version 3. |
| Experiments | We parsed the language model training data with Berkeley parser, and then trained a dependency language model based on the parsing output. |
| Conclusion | 9We used about 70k sentence pairs for CE model training , while Wang et a1. |
| Conclusion | (2008) used about 100k sentence pairs, a CE translation dictionary and more monolingual corpora for model training . |
| Experiments | Table 2 describes the data used for model training in this paper, including the BTEC (Basic Travel Expression Corpus) Chinese-English (CE) corpus and the BTEC English-Spanish (ES) corpus provided by IWSLT 2008 organizers, the HIT olympic CE corpus (2004-863-008)1 and the Europarl ES corpusz. |
| Experiments | Here, we used the synthetic CE Olympic corpus to train a model, which was interpolated with the CE model trained with both the BTEC CE1 corpus and the synthetic BTEC corpus to obtain an interpolated CE translation model. |
| Experiments | While in the subtable below, JST F1 is also undefined since the model trained on PD gives a POS set different from that of CTB. |
| Experiments | We also see that for both segmentation and Joint S&T, the performance sharply declines when a model trained on PD is tested on CTB (row 2 in each subtable). |
| Experiments | This obviously fall behind those of the models trained on CTB itself (row 3 in each subtable), about 97% F1, which are used as the baselines of the following annotation adaptation experiments. |
| Experiments of Parsing | Models Training data (%) (%) (%) GP CTB 79.9 82.2 81.0 RP CTB 82.0 84.6 83.3 |
| Experiments of Parsing | All the sentences LR LP F Models Training data (%) (%) (%) |
| Experiments of Parsing | LR LP F Models Training data (%) (%) (%) |
| Experiments | Figure 6: Mean reciprocal ratio on Xinhua test set vs. alignment entropy and F-score for models trained with different affinity alignments. |
| Experiments | Figure 7: Mean reciprocal ratio on Xinhua test set vs. alignment entropy and F-score for models trained with different phonological alignments. |
| Related Work | Although the direct orthographic mapping approach advocates a direct transfer of grapheme at runtime, we still need to establish the grapheme correspondence at the model training stage, when phoneme level alignment can help. |