Design Challenges in Low-resource Cross-lingual Entity Linking (2020)

Abstract: Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques. However, current techniques do not rise to the challenges introduced by text in low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained. This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention. Our analysis indicates that current methods are limited by their reliance on Wikipedia’s interlanguage links and thus suffer when the foreign language’s Wikipedia is small. We conclude that the LRL setting requires the use of outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot XEL system, QuEL, that utilizes search engines query logs. With experiments on 25 languages, QuEL shows an average increase of 25% in gold candidate recall and of 13% in end-to-end linking accuracy over state-of-the-art baselines.

## introduction cross-lingual entity linking (xel) aims at grounding mentions written in a foreign (source) language (sl) into entries in a (target) language knowledge base (kb), which we consider here as the english wikipedia following @xcite ; @xcite ; @xcite . in figure 1 , for instance, an odia (an indo-aryan language in india) mention ("chilika lake") is linked to the corresponding english wikipedia entry. the xel figure 1 : the exl task: in the given sentence we link "chilika lake" to its corresponding english wikipedia task typically involves two main steps: (1) candidate generation, retrieving a list of candidate kb entries for the mention, and (2) candidate ranking, selecting the most likely entry from the candidates. while xel techniques have been studied heavily in recent years, many challenges remain in the lrl setting. specifically, existing candidate generation methods perform well on wikipedia-based dataset but fail to generalize beyond wikipedia, to news and social media text. error analysis on existing lrl xel systems shows that the key obstacle is candidate generation. for example, 79.3%-89.1% of xel errors in odia can be attributed to the limitations of candidate generation. in this paper, we present a thorough analysis of the limitations of several leading candidate generation methods. although these methods adopt different techniques, we find that all of them heavily rely on wikipedia interlanguage links foot_0 as their crosslingual resources. however, small sl wikipedia size limits their performance in the lrl setting. as shown in figure 2 , while the core challenge of lrl xel is to link lrl entities (a) to candidates in the english wikipedia (c), interlanguage links only map a small subset of the lrl entities that appear in both lrl wikipedia (b) and english wikipedia. therefore, methods that only leverage interlanguage links (b ∩ c) as the main source of supervision cannot cover a wide range of entities. for example, the amharic wikipedia has 14,854 entries, but only 8,176 of them have interlanguage links to english. furthermore, as we show, existing candidate generation methods perform well on wikipedia-based datasets but fail to generalize to outside-wikipedia text such as news or social media. ## candidate generation analysis in this section, we analyze four leading candidate generation methods: p(e|m), xlwikifier, name trans, pivoting, and translit(see method p(e|m)name transpivotingtranslit system usage xlwikifier --xelms ---elisa ---pbel plus --cross-lingual resource title map anchor text --other lang --- ## experiments: system comparison given the analysis of the key candidate generation process in section 3, this section moves to study its implications on the overall performance of different lrl xel systems. we first propose our lrl xel framework, quel, by combining quel cg with a zero-shot candidate ranking module. our experimental goal is to compare all systems on both outside-wikipedia data and wikipedia data. we further analyze the entity distribution and entity type on the linking results. in addition, an ablation study is demonstrated in appendix a.3. ## conclusion this work provides a thorough analysis of existing lrl xel techniques, focusing on the step of generating english candidates for foreign language mentions. the analysis identifies the inherent lack of sufficient inter-lingual supervision signals as a key shortcoming of the current approach. this leads to proposing a rather simple method that leverages query logs, that are highly effective in addressing these challenges. given that our experiments show a 25% increase in the candidate generation, one future research direction is to improve candidate ranking in lrl by incorporating coherence statistics and entity types. moreover, given the effectiveness of query logs, we believe it can be applied to other cross-lingual tasks like relation extraction and knowledge base completion.