Deep Generative Model for Joint Alignment and Word RepresentationCode available from https://github.com/uva-slpl/embedalign
Table 8: English sentence evaluation results: the last four rows correspond to the mean of 10 runs with EmbedAlign models. All models, but w2vec, employ bidirectional encoders.
['Model', 'MR', 'CR', 'SUBJ', 'MPQA', 'SST', 'TREC', 'MRPC', 'SICK-R', 'SICK-E', 'SST14']
For comparison, we report a SkipGram model (here indicated as w2vec) as well as a model that uses the encoder of a neural machine translation system (NMT) trained on English-French Europarl data. In both cases, we report results by Conneau et al. We report EmbedAlign trained on either En-Fr or En-De. The last line (Combo) shows what happens if we train logistic regression on the concatenation of embeddings inferred by both EmbedAlign models , that is, En-Fr and En-De. Note that these two systems perform sometimes better sometimes worse depending on the benchmark. There is no clear pattern, but differences may well come from some qualitative difference in the induced latent space. It is a known fact that different languages realise lexical ambiguities differently, thus representations induced towards different languages are likely to capture different generalisations. As Combo results show, the representations induced from different corpora are somewhat complementary. Once more we report a monolingual variant of EmbedAlign (indicated by En) in an attempt to illustrate how crucial the translation signal is.