English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Contextual synonym knowledge is crucial for those similarity-oriented taskswhose core challenge lies in capturing semantic similarity between entities intheir contexts, such as entity linking and entity matching. However, mostPre-trained Language Models (PLMs) lack synonym knowledge due to inherentlimitations of their pre-training objectives such as masked language modeling(MLM). Existing works which inject synonym knowledge into PLMs often sufferfrom two severe problems: (i) Neglecting the ambiguity of synonyms, and (ii)Undermining semantic understanding of original PLMs, which is caused byinconsistency between the exact semantic similarity of the synonyms and thebroad conceptual relevance learned from the original corpus. To address theseissues, we propose PICSO, a flexible framework that supports the injection ofcontextual synonym knowledge from multiple domains into PLMs via a novelentity-aware Adapter which focuses on the semantics of the entities (synonyms)in the contexts. Meanwhile, PICSO stores the synonym knowledge in additionalparameters of the Adapter structure, which prevents it from corrupting thesemantic understanding of the original PLM. Extensive experiments demonstratethat PICSO can dramatically outperform the original PLMs and the otherknowledge and synonym injection models on four different similarity-orientedtasks. In addition, experiments on GLUE prove that PICSO also benefits generalnatural language understanding tasks. Codes and data will be public.

Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge