Coreference Resolution

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Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

16 leaderboard tables shown for this task, 16 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 16 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

45 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 45 until expanded.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 288 papers with code (880 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Attention Is All You Need
  12 Jun 2017 595 repositories listed Syntology ran 600 of 946 samples · 346 unverified · 451 pointer-only (licence)
  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
  11 Oct 2018 534 repositories listed Syntology ran 204 of 659 samples · 455 unverified · 149 pointer-only (licence)
  We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
• Language Models are Few-Shot Learners
  28 May 2020 67 repositories listed Syntology ran 15 of 65 samples · 50 unverified · 4 pointer-only (licence)
  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
  23 Oct 2019 57 repositories listed Syntology ran 2 of 31 samples · 29 unverified
  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
  15 Feb 2018 46 repositories listed Syntology ran 23 of 58 samples · 35 unverified · 25 pointer-only (licence)
  We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.
• Language Models are Unsupervised Multitask Learners
  14 Feb 2019 21 repositories listed
  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
  5 Jun 2020 14 repositories listed Syntology ran 4 of 13 samples · 9 unverified · 3 pointer-only (licence)
  Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
• WinoGrande: An Adversarial Winograd Schema Challenge at Scale
  24 Jul 2019 10 repositories listed
  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…
• Scaling Instruction-Finetuned Language Models
  20 Oct 2022 9 repositories listed Syntology ran 8 of 17 samples · 9 unverified · 2 pointer-only (licence)
  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…
• Finetuned Language Models Are Zero-Shot Learners
  3 Sep 2021 8 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
• PaLM: Scaling Language Modeling with Pathways
  5 Apr 2022 7 repositories listed Syntology ran 30 of 37 samples · 7 unverified
  To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
• SpanBERT: Improving Pre-training by Representing and Predicting Spans
  24 Jul 2019 6 repositories listed Syntology ran 3 of 15 samples · 12 unverified · 4 pointer-only (licence)
  We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text.
• SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
  10 Dec 2018 6 repositories listed
  Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
• Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
  16 Mar 2020 5 repositories listed Syntology ran 16 of 30 samples · 14 unverified · 28 pointer-only (licence)
  We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
• Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
  29 Aug 2018 5 repositories listed
  We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
• Higher-order Coreference Resolution with Coarse-to-fine Inference
  15 Apr 2018 5 repositories listed Syntology ran 2 of 23 samples · 21 unverified
  We introduce a fully differentiable approximation to higher-order inference for coreference resolution.
• Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
  3 Apr 2023 4 repositories listed
  How do large language models (LLMs) develop and evolve over the course of training?
• Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
  11 Oct 2018 4 repositories listed
  Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge.
• Gender Bias in Coreference Resolution
  25 Apr 2018 4 repositories listed
  We present an empirical study of gender bias in coreference resolution systems.
• Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
  18 Apr 2018 4 repositories listed
  We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias.
• End-to-end Neural Coreference Resolution
  21 Jul 2017 4 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector.
• Hungry Hungry Hippos: Towards Language Modeling with State Space Models
  28 Dec 2022 3 repositories listed Syntology ran 7 of 15 samples · 8 unverified
  First, we use synthetic language modeling tasks to understand the gap between SSMs and attention.
• Ask Me Anything: A simple strategy for prompting language models
  5 Oct 2022 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  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…
• ST-MoE: Designing Stable and Transferable Sparse Expert Models
  17 Feb 2022 3 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  But advancing the state-of-the-art across a broad set of natural language tasks has been hindered by training instabilities and uncertain quality during fine-tuning.
• The MultiBERTs: BERT Reproductions for Robustness Analysis
  30 Jun 2021 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Experiments with pre-trained models such as BERT are often based on a single checkpoint.
• Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
  6 Oct 2020 3 repositories listed
  Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models.
• An Annotated Dataset of Coreference in English Literature
  3 Dec 2019 3 repositories listed
  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.
• Multi-hop Question Answering via Reasoning Chains
  7 Oct 2019 3 repositories listed
  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.
• A Hybrid Neural Network Model for Commonsense Reasoning
  27 Jul 2019 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  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.
• Labeling supervised fine-tuning data with the scaling law
  5 May 2024 2 repositories listed
  Our contributions are: 1) Created Supervised Fine-Tuning (SFT) training data in alpaca format, along with a set of Low-Rank Adaptation (LoRA) weights, and 2) Developed a method for acquiring high-quality data leveraging…

Syntology lines on 18 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.