text: 72,209,1313754611770167296,3832040415,Ran Zmigrod,"⚠️ Attention all NLPers, when decoding dependency trees, please mind the root! ⚠️ Check out our new @emnlp2020 short paper about efficient root-constrained decoding for graph-based dependency parsers! 🌲 <LINK> Joint work with @xtimv and @ryandcotterell <LINK> Edge-factored non-projective dependency parsing decoding is done using MST algorithms. However, most dependency tree annotation standards do not directly translate to spanning trees! A subtle root-constraint is often required for dependency trees, only one edge may emanate from the root! The current NLP solution to this is to add a factor of n to the runtime. We introduce the NLP community to an algorithm from the 80s that correctly decodes root-constrained dependency trees without sacrificing runtime. 🌴 Our code is available at <LINK>",https://arxiv.org/abs/2010.02550,"The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases. In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime. ",Please Mind the Root: Decoding Arborescences for Dependency Parsing,5,"['⚠️ Attention all NLPers, when decoding dependency trees, please mind the root! ⚠️\nCheck out our new @emnlp2020 short paper about efficient root-constrained decoding for graph-based dependency parsers!\n🌲 <LINK>\nJoint work with @xtimv and @ryandcotterell <LINK>', 'Edge-factored non-projective dependency parsing decoding is done using MST algorithms. However, most dependency tree annotation standards do not directly translate to spanning trees!', 'A subtle root-constraint is often required for dependency trees, only one edge may emanate from the root! The current NLP solution to this is to add a factor of n to the runtime.', 'We introduce the NLP community to an algorithm from the 80s that correctly decodes root-constrained dependency trees without sacrificing runtime.', '🌴 Our code is available at https://t.co/6G7CMgeDcm']",20,10,801