  Getting an overview over the legal domain has become challenging, especially
in a broad, international context. Legal question answering systems have the
potential to alleviate this task by automatically retrieving relevant legal
texts for a specific statement and checking whether the meaning of the
statement can be inferred from the found documents. We investigate a
combination of the BM25 scoring method of Elasticsearch with word embeddings
trained on English translations of the German and Japanese civil law. For this,
we define criteria which select a dynamic number of relevant documents
according to threshold scores. Exploiting two deep learning classifiers and
their respective prediction bias with a threshold-based answer inclusion
criterion has shown to be beneficial for the textual entailment task, when
compared to the baseline.
