Coreference Resolution
285 papers with code • 16 benchmarks • 45 datasets
Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
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I voted for Obama because he was most aligned with my values", she said.
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"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
Benchmarks
These leaderboards are used to track progress in Coreference Resolution
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Libraries
Use these libraries to find Coreference Resolution models and implementationsDatasets
WSC
OntoNotes 5.0
WinoBias
GAP Coreference Dataset
Quoref
Most implemented papers
Attention Is All You Need
tensorflow/tensor2tensor
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NeurIPS 2017
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
huggingface/transformers
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arXiv 2019
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Language Models are Unsupervised Multitask Learners
openai/gpt-2
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Preprint 2019
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
microsoft/DeBERTa
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Scaling Instruction-Finetuned Language Models
We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
The key steps of the dataset construction consist of (1) a carefully designed crowdsourcing procedure, followed by (2) systematic bias reduction using a novel AfLite algorithm that generalizes human-detectable word associations to machine-detectable embedding associations.
Finetuned Language Models Are Zero-Shot Learners
google-research/flan
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ICLR 2022
We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
