Coreference Resolution
261 papers with code • 16 benchmarks • 43 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
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.
A Hybrid Neural Network Model for Commonsense Reasoning
An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERT-based contextual encoder but use different model-specific input and output layers.
Multi-hop Question Answering via Reasoning Chains
soujanyarbhat/aNswER_multirc
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Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way.
An Annotated Dataset of Coreference in English Literature
We present in this work a new dataset of coreference annotations for works of literature in English, covering 29, 103 mentions in 210, 532 tokens from 100 works of fiction.
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
shtoshni92/long-doc-coref
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EMNLP 2020
Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models.
The MultiBERTs: BERT Reproductions for Robustness Analysis
google-research/language
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ICLR 2022
Experiments with pre-trained models such as BERT are often based on a single checkpoint.
Ask Me Anything: A simple strategy for prompting language models
Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task.
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
hazyresearch/h3
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First, we use synthetic language modeling tasks to understand the gap between SSMs and attention.
Dynamic Entity Representations in Neural Language Models
Understanding a long document requires tracking how entities are introduced and evolve over time.
A Simple Method for Commonsense Reasoning
Commonsense reasoning is a long-standing challenge for deep learning.
