What you can cram into a single $&!#⁢* vector:Probing sentence embeddings for linguistic properties
Table 3: Test results for training tasks. Figure of merit is BLEU score for NMT and accuracy for Seq2Tree and NLI.
['[BOLD] Model', '[BOLD] En-Fr', '[BOLD] En-De', '[BOLD] En-Fi', '[BOLD] Seq2Tree', '[BOLD] NLI']
For NMT and Seq2Tree, we left out two random sets of 10k sentences from the training data for dev and test. The NLI dev and test sets are the ones of SNLI. Observe how results are similar for the three encoders, while, as discussed in the main text, they differ in terms of the linguistic properties their sentence embeddings are capturing. The last row of the table reports BLEU scores for our BiLSTM architecture trained with attention, showing that the architecture is on par with current NMT models, when attention is introduced. For comparison, our attention-based model obtains 37 BLEU score on the standard WMT’14 En-Fr benchmark.