To browse Academia. This paper introduces a new task of crosslingual slot filling which aims to discover attributes for entity queries from crosslingual comparable corpora and then present answers in a desired language. It is a very challenging task which suffers from both information extraction and machine translation errors. In this paper we analyze the types of errors produced by five different baseline approaches, and present a novel supervised rescoring based validation approach to incorporate global evidence from very large bilingual We describe an efficient way to create a test collection for evaluating the accuracy of cross-language entity linking.
Queries are created by semi-automatically identifying person names on the English side of a parallel corpus, using judgments obtained through crowdsourcing to identify the entity corresponding to the name, and projecting the English name onto the non-English document using word alignments. We applied the technique to produce the first publicly available multilingual cross-language entity linking collection.
Cross-lingual Entity Typing CLET aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages.
In this paper, by utilizing multilingual transfer learning via the mixture-of-experts approach, our model dynamically capture the relationship between target language and each source language, and effectively generalize to predict types of unseen entities in new languages.
Extensive experiments on multilingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer. We questioned the relationship between language similarity and the performance of CLET.
