ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
Table 2: Comparison of precision@1 between ABSent (A), uni-Sent (U), and further modified settings. A∖mis, U∖mis are two variants without mismatch loss term. As swaps the weighting strategy (TF-IDF weighting for Tatoeba but not for Europarl). Aw stands for first aligning word-level representations and then generating sentence embeddings.
['Dataset Corpus', '[BOLD] Tatoeba deu→', '[BOLD] Tatoeba eng→', '[BOLD] Tatoeba spa→', '[BOLD] Tatoeba eng→', '[BOLD] Europarl deu→', '[BOLD] Europarl eng→', '[BOLD] Europarl spa→', '[BOLD] Europarl eng→']
S4SS3SSS0Px3 Influence of Mismatch Loss. This experiment aims to study how this loss function influences the effectiveness of our proposed models. For simplicity, we refer to a model X without the mismatch loss as X∖mis, where X can be either uni-Sent or ABSent. We take 10% of parallel training pairs, leaving other parameters as in the main experiments. Generally, bringing in mismatch pairs into the training improves the effectiveness of both our ABSent model and the uni-Sent version. Choosing an appropriate weighting strategy boosts the experimental results. Since TF-IDF weights words in accordance with their assumed importance, for the Europarl corpus, the 2σ volume of sentence lengths falls between 35 and 60, while for the Tatoeba corpus it is between 8 and 16. In such circumstances, words that appear more frequently in long sentences, especially function words such as ’a’, ’the’, etc. ought to have a lower weight, while infrequent ones ought to have a higher weight. For short sentences, it may make sense to even consider words with higher frequency, so as to not neglect their semantic contribution to the sentence. Thus, simple averaging works better than TF-IDF weighted averaging for the Tatoeba corpus.