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natural-language-processing

Coreference Resolution

3260 papers • 126 benchmarks • 313 datasets

Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities. Example: +-----------+ | | I voted for Obama because he was most aligned with my values", she said. | | | +-------------------------------------------------+------------+ "I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.

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Benchmarks

These leaderboards are used to track progress in coreference-resolution

Trend

Dataset

Best Model

Actions

Winograd Schema Challenge

Winograd Schema Challenge

OntoNotes

OntoNotes

CoNLL 2012

Libraries

i

Use these libraries to find coreference-resolution models and implementations

huggingface/transformers

5 papers 124,527

Datasets

WSC

OntoNotes 5.0

CoNLL

WinoBias

GAP Coreference Dataset

GAP Coreference Dataset

CoNLL-2012

Subtasks

coreference-resolution Cross Document Coreference Resolution

Most implemented papers

Attention is All you Need

Noam Shazeer, Ashish Vaswani, Lukasz Kaiser, Jakob Uszkoreit, Niki Parmar, I. Polosukhin, Llion Jones, Aidan N. Gomez•Sun Jun 11 2017

A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

164803

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

CoNLL 2012

GAP

GAP

STM-coref

STM-coref

DWIE

DWIE

OntoGUM

OntoGUM

XWinograd EN

XWinograd EN

XWinograd FR

XWinograd FR

CoNLL12

CoNLL12

WikiCoref

WikiCoref

The ARRAU Corpus

The ARRAU Corpus

LitBank

LitBank

PreCo

PreCo

Quizbowl

Quizbowl

shreyashankar/gpt3-sandbox

3 papers 2,902

3 papers 2,548

volcengine/vegiantmodel

3 papers 196

abhaskumarsinha/MinimalGPT

3 papers 19

labmlai/annotated_deep_learning_pap…

2 papers 47,519

bigscience-workshop/promptsource

2 papers 2,477

re-search/DocProduct

2 papers 559

kpot/keras-transformer

2 papers 530

2 papers 527

thu-keg/omnievent

2 papers 311

helboukkouri/character-bert

2 papers 194

chiayewken/bert-qa

2 papers 166

manideep2510/siamese-BERT-fake-news…

2 papers 132

reppy4620/Dialog

2 papers 73

duyunshu/bert-sentiment-analysis

2 papers 52

wangcongcong123/ttt

2 papers 37

ypeleg/MinimalIsAllYouNeed

2 papers 29

brightmart/bert_customized

2 papers 25

flxst/nerblackbox

2 papers 22

Colanim/BERT_STS-B

2 papers 20

guoyaohua/BERT-Chinese-Annotation

2 papers 17

2 papers 11

xiaopingzhong/bert-finetune-for-cla…

2 papers 11

SYangDong/bert-with-frozen-code

2 papers 10

ECB+

ECB+

GAP

Quoref

English Web Treebank

English Web Treebank

0

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin, Kenton Lee, Kristina Toutanova, Ming-Wei Chang•Mon Dec 31 2018

A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

109344 0

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Noam Shazeer, Peter J. Liu, Sharan Narang, Yanqi Zhou, Adam Roberts, Colin Raffel, Katherine Lee, Michael Matena, Wei Li•Tue Oct 22 2019

This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

24077 0

Language Models are Few-Shot Learners

I. Sutskever, Tom B. Brown, Alec Radford, Christopher Berner, Christopher Hesse, Scott Gray, Dario Amodei, A. Ramesh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Jack Clark, Gretchen Krueger, T. Henighan, Benjamin Mann, Sam McCandlish, Prafulla Dhariwal, Jeff Wu, Daniel M. Ziegler, Arvind Neelakantan, Mark Chen, Nick Ryder, Clemens Winter, Ariel Herbert-Voss, Melanie Subbiah, J. Kaplan, Pranav Shyam, R. Child, Eric Sigler, Ma-teusz Litwin, Benjamin Chess•Wed May 27 2020

GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.

53675 0

Deep Contextualized Word Representations

Christopher Clark, Mohit Iyyer, Luke Zettlemoyer, Kenton Lee, Matt Gardner, Matthew E. Peters, Mark Neumann•Wed Feb 14 2018

A new type of deep contextualized word representation is introduced that models both complex characteristics of word use and how these uses vary across linguistic contexts, allowing downstream models to mix different types of semi-supervision signals.

12011 0

SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering

Xuedong Huang, Michael Zeng, Chenguang Zhu•Sun Dec 09 2018

An innovated contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models, which leverages both inter-attention and self-att attention to comprehend conversation context and extract relevant information from passage.

131 0

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Jianfeng Gao, Weizhu Chen, Xiaodong Liu, Pengcheng He•Thu Jun 04 2020

A new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) is proposed that improves the BERT and RoBERTa models using two novel techniques that significantly improve the efficiency of model pre-training and performance of downstream tasks.

3483 0

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Danqi Chen, Daniel S. Weld, Omer Levy, Luke Zettlemoyer, Mandar Joshi, Yinhan Liu•Tue Jul 23 2019

The approach extends BERT by masking contiguous random spans, rather than random tokens, and training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it.

2111 0

Scaling Instruction-Finetuned Language Models

Quoc V. Le, J. Dean, Xinyun Chen, Yanping Huang, Andrew M. Dai, Sharan Narang, W. Fedus, S. Gu, Mostafa Dehghani, Barret Zoph, A. Chowdhery, Mirac Suzgun, Adam Roberts, Jacob Devlin, Slav Petrov, S. Longpre, Hongkun Yu, Yi Tay, Hyung Won Chung, Ed H. Chi, Denny Zhou, Jason Wei, Xuezhi Wang, Albert Webson, Siddhartha Brahma, Le Hou, Gaurav Mishra, Eric Li, Zhuyun Dai, Dasha Valter, Adams Wei Yu, Vincent Y. Zhao•Wed Oct 19 2022

It is found that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups, and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).

3866 0

Adding a benchmark result helps the community track progress.

Coreference Resolution | State-of-the-Art