Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking
Table 4: Performance comparison on the TAC 2015 Es and Zh datasets. EL + En Coref outperforms the best 2015 TAC system (Rank 1) without requiring any Es or Zh coreference data.
['Systems', 'Train on Target Lang', 'Acc. on Es', 'Acc. on Zh']
EL Results : We replace the in-document coreference system (trained on the target language) of Sil18 with our En model to investigate the performance of our proposed algorithm on an extrinsic task. “EL - Coref” refers to the case where the first step of coreference is not used and EL is used to link the mentions directly to Freebase. “EL + En Coref” refers to the case where the neural english coreference model is first used on Zh or Es data followed by the EL model. The former is 3 points below the latter on Es and 2.6 points below Zh, implying coreference is a vital task for EL. Our “EL + En Coref” outperforms the 2015 TAC best system by 0.7 points on Es and 0.8 points on Zh, without requiring any training data for coreference on Es and Zh respectively. Finally, we show the SOTA results on these two data sets recently reported by Sil18. Although their EL model does not use any supervision from Es or Zh, their coreference resolution model is trained on a large internal data set on the same language as the test set .Without using any in-language training data, our results are competitive to their results (1.2% below on Es and 0.5% below on Zh).