| A Joint Model with Unlabeled Parallel Text | The intuition here is that if the translation probability of two sentences is high, the probability that they have the same sentiment label should be high as well. |
| A Joint Model with Unlabeled Parallel Text | where p(a,-) is the translation probability of the i-th sentence pair in U :2 37’ is the opposite of y’; the first term models the probability that x}, and x5, have the same label; and the second term models the probability that they have different labels. |
| Experimental Setup 4.1 Data Sets and Preprocessing | Because sentence pairs in the ISI corpus are quite noisy, we rely on Giza++ (Och and Ney, 2003) to obtain a new translation probability for each sentence pair, and select the 100,000 pairs with the highest translation probabilities.5 |
| Experimental Setup 4.1 Data Sets and Preprocessing | We first obtain translation probabilities for both directions (i.e. |
| Results and Analysis | Preliminary experiments showed that Equation 5 does not significantly improve the performance in our case, which is reasonable since we choose only sentence pairs with the highest translation probabilities to be our unlabeled data (see Section 4.1). |
| Experiments | In this section, we propose a method to modify the translation probabilities of the t-table by interpolating the translation counts with transliteration counts. |
| Experiments | We combine the transliteration probabilities with the translation probabilities of the IBM models and the HMM model. |
| Experiments | The normal translation probability pta( f |e) of the word alignment models is computed with relative frequency estimates. |