Learning to Represent Bilingual Dictionaries
Table 3: Cross-lingual reverse dictionary retrieval results by BilDRL variants. We report P@1, P@10, and MRR on four groups of models: (i) basic dictionary models that adopt four different encoding techniques (BOW, CNN, GRU and ATT); (ii) models with the two best encoding techniques that enforce the monolingual retrieval approach by Hill et al. Hill et al. (2016) (GRU-mono and ATT-mono); (iii) models adopting bilingual multi-task learning (GRU-MTL and ATT-MTL); (iv) joint learning that employs the best dictionary model of ATT-MTL (ATT-joint).
['Languages Metric', 'En-Fr  [ITALIC] P@1', 'En-Fr  [ITALIC] P@10', 'En-Fr  [ITALIC] MRR', 'Fr-En  [ITALIC] P@1', 'Fr-En  [ITALIC] P@10', 'Fr-En  [ITALIC] MRR', 'En-Es  [ITALIC] P@1', 'En-Es  [ITALIC] P@10', 'En-Es  [ITALIC] MRR', 'Es-En  [ITALIC] P@1', 'Es-En  [ITALIC] P@10', 'Es-En  [ITALIC] MRR']
Results. The first group compares four different encoding techniques for the basic dictionary models. GRU thereof consistently outperforms CNN and BOW, since the latter two fail to capture the important sequential information for descriptions. ATT that weighs among the hidden states has notable improvements over GRU. While we equip the two better encoding techniques with the monolingual retrieval approach (GRU-mono and ATT-mono), we find that the way of learning the dictionary models towards monolingual targets and retrieving cross-lingual related words incurs more impreciseness to the task. For models of the third group that conduct multi-task learning in two directions of a language pair, the results show significant enhancement of performance in both directions. For the final group of results, we incorporate the best variant of multi-task models into the joint learning architecture, which leads to compelling improvement of the task on all settings. This demonstrates that properly adapting the word embeddings in joint with the bilingual dictionary model efficaciously constructs the embedding space that suits better the representation of both bilingual lexical and sentential semantics.