Papers with coreference
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)
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| Challenge: | Existing methods for coreference resolution are based on word2vec-like representations of entities. |
| Approach: | They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers . |
| Outcome: | The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo . |
Evaluating Discourse Phenomena in Neural Machine Translation (N18-1)
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| Challenge: | Existing models for machine translation have been evaluated with standard automatic metrics, but are poorly adapted to evaluating discourse phenomena. |
| Approach: | They propose to use BLEU to train multi-encoder NMT models on English subtitles to test their ability to exploit previous source and target sentences. |
| Outcome: | The proposed multi-encoder models give limited improvements on the coreference and coherence tests. |
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation (N19-1)
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| Challenge: | a corpus of anaphoric information (coreference) is crowdsourced through a game-with-a-purpose . its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. |
| Approach: | They propose to crowdsource anaphoric information corpus by a game-with-a-purpose and to use it to train a coreference resolver. |
| Outcome: | The proposed corpus contains annotations for 108,000 markables and 20 judgments per markable, and 2.2M in total. |
A Probabilistic Annotation Model for Crowdsourcing Coreference (D18-1)
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| Challenge: | Existing methods to generate annotated corpora for coreference are expensive and limited. |
| Approach: | They propose a model of annotation for aggregating crowdsourced anaphoric annotations. |
| Outcome: | The proposed model can extract from crowdsourced annotations coreference chains comparable to those obtained with expert annotation. |
Sequential Cross-Document Coreference Resolution (2021.emnlp-main)
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| Challenge: | Existing models for cross-document coreference resolution have been used for within-document entity coreference but have been relatively limited. |
| Approach: | They propose a model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference. |
| Outcome: | The proposed model achieves competitive results for entity and event coreference while minimizing error propagation in complex reasoning tasks. |
Continual Few-Shot Learning for Text Classification (2021.emnlp-main)
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| Challenge: | a large number of end-to-end systems are needed for many tasks in natural language processing. |
| Approach: | They propose a continual few-shot learning task where a system is asked to correct mistakes with a few training examples. |
| Outcome: | The proposed task compares two NLI and one sentiment analysis datasets with baselines from diverse paradigms. |
Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are intended to reflect human linguistic competencies . but when context is absent or insufficient, ambiguity resolution becomes more tenuous . |
| Approach: | They propose a CORRECT-DETECT trade-off between large language models and ambiguity detection . they show that large language model models can achieve good performance with minimal prompting . |
| Outcome: | The proposed models can achieve good performance with minimal prompting in coreference disambiguation and detection of ambiguity in corefertility tasks, but they cannot do both at the same time. |