CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

By Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, Yuchen Zhang. 2012

869 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

SpanBERT: Improving Pre-training by Representing and Predicting Spans (2019, methodology): “We evaluate on the CoNLL-2012 shared task (Pradhan et al., 2012) for document-level coreference resolution.”
Longformer: The Long-Document Transformer (2020, methodology): “Coreference Resolution We use OntoNotes (Pradhan et al., 2012), and the model from Joshi et al.”
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention (2023, methodology): “We adopt CoNLL03 [56], CoNLL04 [5], CoNLL05 [6], and CoNLL12 [49] as the evaluation datasets.”
What Does BERT Look at? An Analysis of BERT’s Attention (2019, methodology): “We evaluate the attention heads on coreference resolution using the CoNLL-2012 dataset 4 (Pradhan et al., 2012).”
Dynabench: Rethinking Benchmarking in NLP (2021, background): “Progress in NLP has traditionally been measured through a selection of task-level datasets that gradually became accepted benchmarks (Marcus et al., 1993; Pradhan et al., 2012).”
Gender Bias in Neural Natural Language Processing (2018, methodology): “We use the English coreference resolution dataset from the CoNLL-2012 shared task [Pradhan et al., 2012], the benchmark dataset for the training and evaluation of coreference resolution.”
Linguistic Knowledge and Transferability of Contextual Representations (2019, methodology): “We use the dataset from the CoNLL 2012 shared task (Pradhan et al., 2012).”
Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “ks. The table has been adapted from [41]. SOTA results have been taken from [41]; SQUAD [166]: QA task; SNLI [178]: Stanford Natural Language Inference task; SRL [153]: Semantic Role Labelling; Coref [179]: Coreference Resolution; NER [180]: Named Entity Recognition; SST-5 [4]: Stanford Sentiment Tr”
Deep Reinforcement Learning for Mention-Ranking Coreference Models (2016, methodology): “We run experiments on the English and Chinese portions of the CoNLL 2012 Shared Task data (Pradhan et al., 2012) and evaluate with the MUC, B3, and CEAFφ4 metrics.”
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks (2021, neutral): “For multitask setting, the pre-train training set is a combination of the training set of CoNLL05 (Carreras and Màrquez, 2005), CoNLL12 (Pradhan et al., 2012) and propbank-release (a common extend data used for training SRL).”
Named Entity Recognition with Bidirectional LSTM-CNNs (2015, methodology): “ochs, and yet slowly improves starting at epoch 50 and beyond. 3.3 OntoNotes 5.0 Dataset Following Durrett and Klein (2014), we applied our model to the portion of the CoNLL-2012 shared task dataset (Pradhan et al., 2012) with gold-standard named entity annotations; the New Testaments portion was ex”
Easy Victories and Uphill Battles in Coreference Resolution (2013, background): “Inclusion of a hierarchy of 10Discrepancies between scores here and those printed in Pradhan et al. (2012) arise from two sources: improvements to the system of Lee et al. (2011) since the first CoNLL shared task, and a fix to the scoring of B3 in the official scorer since results of the two CoNLL…”
Solving Hard Coreference Problems (2019, background): “Although there is a plethora of works on this task (Soon et al., 2001a; Ng and Cardie, 2002a; Ng, 2004; Bengtson and Roth, 2008; Pradhan et al., 2012; Kummerfeld and Klein, 2013; Chang et al., 2013), it is still deemed an unsolved problem due to intricate and ambiguous nature of natural language…”
Polyglot: Distributed Word Representations for Multilingual NLP (2013, background): “Despite recent momentum towards developing multilingual tools (Nivre et al., 2007; Hajič et al., 2009; Pradhan et al., 2012), most of NLP”