Major Entity Identification: A Generalizable Alternative to Coreference Resolution (2024.emnlp-main)
Copied to clipboard
| Challenge: | Prior work identified annotation differences as one of the main reasons for the limited generalization gap in coreference resolution models. |
| Approach: | They propose an alternative referential task where the target entities are assumed to be specified in the input and the task is limited to the frequent entities. |
| Outcome: | The proposed model generalizes well across domains on multiple datasets with supervised models and LLM-based few-shot prompting. |
Similar Papers
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking (C18-1)
Copied to clipboard
| Challenge: | Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities. |
| Approach: | They propose a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions and a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning. |
| Outcome: | The proposed model outperforms existing models designed for singular mentions and plural mentions. |
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity) |
| Approach: | They propose to join entity linking and coreference resolution in a single structured prediction task over directed trees and use a globally normalized model to solve it. |
| Outcome: | The proposed model improves on two datasets with a +5% boost in accuracy compared to standalone models . the proposed model is based on current models that predict a single antecedent for each span to resolve . |
Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective (2024.findings-acl)
Copied to clipboard
| Challenge: | a recent study shows that evaluations of CR models on multiple datasets conflate different factors concerning what is being measured. |
| Approach: | They propose to view evaluations through the lens of measurement modeling . they show that evaluations risk conflating different factors concerning what is being measured . |
| Outcome: | The evaluations on seven datasets show that models that reflect coreference generalization are often correlated with differences in how coreference is defined and operationalized. |
Active Learning for Coreference Resolution using Discrete Annotation (2020.acl-main)
Copied to clipboard
| Challenge: | Exhaustively annotating coreference is expensive as it requires tracking coreference chains across long passages of text. |
| Approach: | They propose a pairwise annotation technique which asks annotators to identify mention antecedents if a presented mention pair is not coreferent. |
| Outcome: | The proposed method is much more efficient when combined with a mention clustering algorithm for selecting which examples to label . future work can use the proposed protocol to develop coreference models for new domains. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
2*n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult to learn coreference beyond surface matching. |
| Approach: | They propose a heuristic to efficiently filter out a large number of non-coreferent pairs and a training approach on a balanced set of coreferent and non- coreferente mention pairs. |
| Outcome: | The proposed approach significantly reduces compute requirements on two popular ECR datasets while reducing the computational complexity. |
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (2020.emnlp-main)
Copied to clipboard
| Challenge: | despite significant progress on entity coreference resolution, there is a general lack of understanding of what has been improved. |
| Approach: | They present an empirical analysis of entity coreference resolvers to provide an understanding of what has been improved. |
| Outcome: | The proposed model improves the performance of entity coreference resolvers. |
Event Coreference Resolution with Non-Local Information (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing joint models for event coreference resolution are understudied and underexploited . current models only learn trigger detection and event coreference from annotated training data . |
| Approach: | They propose to add a topic-based trigger detection module and a preprocessing module to improve event coreference. |
| Outcome: | The proposed model yields the best results on the KBP 2017 English and Chinese datasets. |
CorefInst: Leveraging LLMs for Multilingual Coreference Resolution (2026.tacl-1)
Copied to clipboard
| Challenge: | Existing methods for CR are encoder-only, decoder-based and asynchronous models. |
| Approach: | They propose a multilingual CR methodology which leverages decoder-only LLMs to handle overt and zero mentions. |
| Outcome: | The proposed model outperforms the leading multilingual CR model by 2 percentage points across all languages in the CorefUD v1.2 dataset. |
Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)
Copied to clipboard
| Challenge: | bridging resolution is the task of recognizing and resolving bridling anaphors in a text. |
| Approach: | They propose a constrained multi-task learning framework for bridging resolution that exploits cross-task consistency constraints to guide the learning process and pre-train the entity coreference model on publicly available coreference data. |
| Outcome: | The proposed model achieves state-of-the-art on three standard evaluation corpora. |