  Transformer-based entity matching methods have significantly moved the state
of the art for less-structured matching tasks such as matching product offers
in e-commerce. In order to excel at these tasks, Transformer-based matching
methods require a decent amount of training pairs. Providing enough training
data can be challenging, especially if a matcher for non-English product
descriptions should be learned. This poster explores along the use case of
matching product offers from different e-shops to which extent it is possible
to improve the performance of Transformer-based matchers by complementing a
small set of training pairs in the target language, German in our case, with a
larger set of English-language training pairs. Our experiments using different
Transformers show that extending the German set with English pairs improves the
matching performance in all cases. The impact of adding the English pairs is
especially high in low-resource settings in which only a rather small number of
non-English pairs is available. As it is often possible to automatically gather
English training pairs from the Web by exploiting schema.org annotations, our
results are relevant for many product matching scenarios targeting low-resource
languages.
