Joint word alignment and bilingual Named Entity Recognition using dual decomposition

Translated bi-texts contain complementary language cues, and previous work on Named Entity Recognition (NER) has demonstrated improvements in performance over monolingual taggers by promoting agreement of tagging decisions between the two languages. However, most previous approaches to bilingual tagging assume word alignments are given as fixed input, which can cause cascading errors. We observe that NER label information can be used to correct alignment mistakes, and present a graphical model that performs bilingual NER tagging jointly with word alignment, by combining two monolingual tagging models with two unidirectional alignment models. We introduce additional cross-lingual edge factors that encourage agreements between tagging and alignment decisions. We design a dual decomposition inference algorithm to perform joint decoding over the combined alignment and NER output space. Experiments on the OntoNotes dataset demonstrate that our method yields significant improvements in both NER and word alignment over state-of-the-art monolingual baselines.