Query by Example for Cross-Lingual Event Retrieval

We propose a Query by Example (QBE) setting for cross-lingual event retrieval. In this setting, a user describes a query event using example sentences in one language, and a retrieval system returns a ranked list of sentences that describe the query event, but from a corpus in a different language. One challenge in this setting is that a sentence may mention more than one event. Hence, matching the query sentence with document sentence results in a noisy matching. We propose a Semantic Role Labeling (SRL) based approach to identify event spans in sentences and use a state-of-the-art sentence matching model, Sentence BERT (SBERT) to match event spans in queries and documents without any supervision. To evaluate our approach we construct an event retrieval dataset from ACE which is an existing event detection dataset. Experimental results show that it is valuable to predict event spans in queries and documents and our proposed unsupervised approach achieves superior performance compared to Query Likelihood (QL), Relevance Model 3 (RM3) and SBERT.