  Entity and relation extraction is a key task in information extraction, where
the output can be used for downstream NLP tasks. Existing approaches for entity
and relation extraction tasks mainly focus on the English corpora and ignore
other languages. Thus, it is critical to improving performance in a
multilingual setting. Meanwhile, multilingual training is usually used to boost
cross-lingual performance by transferring knowledge from languages (e.g.,
high-resource) to other (e.g., low-resource) languages. However, language
interference usually exists in multilingual tasks as the model parameters are
shared among all languages. In this paper, we propose a two-stage multilingual
training method and a joint model called Multilingual Entity and Relation
Extraction framework (mERE) to mitigate language interference across languages.
Specifically, we randomly concatenate sentences in different languages to train
a Language-universal Aggregator (LA), which narrows the distance of embedding
representations by obtaining the unified language representation. Then, we
separate parameters to mitigate interference via tuning a Language-specific
Switcher (LS), which includes several independent sub-modules to refine the
language-specific feature representation. After that, to enhance the relational
triple extraction, the sentence representations concatenated with the relation
feature are used to recognize the entities. Extensive experimental results show
that our method outperforms both the monolingual and multilingual baseline
methods. Besides, we also perform detailed analysis to show that mERE is
lightweight but effective on relational triple extraction and mERE{} is easy to
transfer to other backbone models of multi-field tasks, which further
demonstrates the effectiveness of our method.
