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Learning Better Name Translation for Cross-Lingual Wikification

AAAI 2018 · Large language models › Language, safety and interpretability · approach: Sign and cross-lingual translation · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use joint word alignment + word transliteration model instead of best baseline (P&R with f-align)name translation · Wikipedia title translation avgfuzzy F1: 75.45 -> 78.22Helped
joint model vs P&R f-align on organization namesname translation · Wikipedia title translation, ORG avgfuzzy F1: 69.22 -> 73.14Helped
joint model vs P&R f-align on person namesname translation · Wikipedia title translation, PER avgfuzzy F1: 83.54 -> 84.45Helped
joint model vs P&R f-align for candidate generationcross-lingual wikification candidate generation · avg of 9 languages% gold title in candidates: 57.19 -> 60.12Helped
joint model vs P&R f-align for candidate generation on Germancandidate generation · German% gold title in candidates: 61.63 -> 61.37Hurt
add proposed name translation model to base TAC 2016 EDL systemcross-lingual wikifier · TAC 2016 EDL SpanishF1: 53.71 -> 60.67Helped
add proposed name translation model to base TAC 2016 EDL systemcross-lingual wikifier · TAC 2016 EDL ChineseF1: 63.62 -> 65.53Helped
align words with a word alignment model (f-align) instead of position (p-align) for baseline transliteration modelsP&R · Wikipedia title translation avgfuzzy F1: 73.30 -> 75.45Helped
higher fuzzy F1 does not imply better candidate generation (NMT-char on Spanish LOC)NMT-char · Spanish LOCfuzzy F1 vs candidate coverage: 73.56 vs 27.86Mixed

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