Learning to Represent Bilingual Dictionaries
Table 4: Accuracy and F1-scores of bilingual paraphrase identification. For BilDRL, the results by three model variants are reported: BilDRL-GRU-MTL and BilDRL-ATT-MTL are models with bilingual multi-task learning, and BilDRL-ATT-joint is the best ATT-based dictionary model variant deployed with both multi-task and joint learning.
['Languages Metrics', 'En&Fr  [ITALIC] Acc.', 'En&Fr  [ITALIC] F1', 'En&Es  [ITALIC] Acc.', 'En&Es  [ITALIC] F1']
Results. This task is challenging due to the heterogeneity of cross-lingual paraphrases and limitedness of learning resources. We believe that it comes down to the fact that sentences of different languages are often drastically heterogenous in both lexical semantics and the sentence grammar that governs the composition of words. Hence, it is not surprising that previous neural sentence pair models, which capture the semantic relation of bilingual sentences directly from all participating words, fall short at the multilingual task. BilDRL, however, effectively leverages the correspondence of lexical and sentential semantics to simplify the task to an easier entailment task in the lexicon space, for which the multi-task learning BilDRL-ATT-MTL outperforms the best baseline respectively by 3.80% and 4.80% of accuracy in both language settings, while BilDRL-ATT-joint, employing the joint learning, further improves the task by another satisfying 3.26% and 1.06% of accuracy. Both also show notable increment in F1.