  A key feature of neural models is that they can produce semantic vector
representations of objects (texts, images, speech, etc.) ensuring that similar
objects are close to each other in the vector space. While much work has
focused on learning representations for other modalities, there are no aligned
cross-modal representations for text and knowledge base (KB) elements. One
challenge for learning such representations is the lack of parallel data, which
we use contrastive training on heuristics-based datasets and data augmentation
to overcome, training embedding models on (KB graph, text) pairs. On WebNLG, a
cleaner manually crafted dataset, we show that they learn aligned
representations suitable for retrieval. We then fine-tune on annotated data to
create EREDAT (Ensembled Representations for Evaluation of DAta-to-Text), a
similarity metric between English text and KB graphs. EREDAT outperforms or
matches state-of-the-art metrics in terms of correlation with human judgments
on WebNLG even though, unlike them, it does not require a reference text to
compare against.
