@inproceedings {5628,
	title = {Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks},
	year = {2017},
	month = {April},
	pages = {331{\textendash}336},
	publisher = {Association for Computational Linguistics},
	address = {Valencia, Spain},
	abstract = {<p>We introduce a constituency parser based on a bi-LSTM encoder adapted from re- cent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character bi- LSTM (Ballesteros et al., 2015; Plank et al., 2016). We model two important in- terfaces of constituency parsing with aux- iliary tasks supervised at the word level: (i) part-of-speech (POS) and morpholog- ical tagging, (ii) functional label predic- tion. On the SPMRL dataset, our parser obtains above state-of-the-art results on constituency parsing without requiring ei- ther predicted POS or morphological tags, and outputs labelled dependency trees.</p>
},
	url = {http://www.aclweb.org/anthology/E17-2053},
	author = {Coavoux, Maximin and Beno{\^\i}t Crabb{\'e}}
}
