331 citations · 478 across the 17 of their papers we have counts for
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Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited
Artur Kulmizev, Miryam de Lhoneux, Johannes Gontrum +2
Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural feat…
Encoders Help You Disambiguate Word Senses in Neural Machine Translation
Gongbo Tang, Rico Sennrich, Joakim Nivre
Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of…
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?
Miryam de Lhoneux, Sara Stymne, Joakim Nivre
There is a growing interest in investigating what neural NLP models learn about language. A prominent open question is the question of whether or not it is necessary to model hiera…
Recursive Subtree Composition in LSTM-Based Dependency Parsing
Miryam de Lhoneux, Miguel Ballesteros, Joakim Nivre
The need for tree structure modelling on top of sequence modelling is an open issue in neural dependency parsing. We investigate the impact of adding a tree layer on top of a seque…