most citedImproving historical spelling normalization with bi-directional LSTMs and multi-task learning

4 citations · 11 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20171 cited

Parsing Universal Dependencies without training

Héctor Martínez Alonso, Željko Agić, Barbara Plank +1

We propose UDP, the first training-free parser for Universal Dependencies (UD). Our algorithm is based on PageRank and a small set of head attachment rules. It features two-step de…

cs.CL20172 cited

Cross-lingual RST Discourse Parsing

Chloé Braud, Maximin Coavoux, Anders Søgaard

Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating mode…

cs.CL20172 cited

Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages

Michael Sejr Schlichtkrull, Anders Søgaard

In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependen…

cs.NE2016

Spikes as regularizers

Anders Søgaard

We present a confidence-based single-layer feed-forward learning algorithm SPIRAL (Spike Regularized Adaptive Learning) relying on an encoding of activation spikes. We adaptively u…

cs.CL20164 cited

Improving historical spelling normalization with bi-directional LSTMs and multi-task learning

Marcel Bollmann, Anders Søgaard

Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling…

cs.CL20162 cited

A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments

Omer Levy, Anders Søgaard, Yoav Goldberg

While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remain vague. We observe that whethe…