| Challenge: | Existing methods to improve coreference resolution use labeled data. |
| Approach: | They propose two self-supervised tasks that are closely related to coreference resolution to improve mention representation. |
| Outcome: | The proposed models improve mention representations by learning them on a GAP dataset. |
Similar Papers
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering (P18-2)
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| Challenge: | Existing methods for identifying and clustering mentions in text are complex and require heuristics to solve. |
| Approach: | They propose to use a biaffine attention model to get antecedent scores for each possible mention and optimize mention detection and mention clustering accuracy given the mention cluster labels. |
| Outcome: | The proposed model achieves the state-of-the-art performance on the CoNLL-2012 shared task English test set. |
Active Learning for Coreference Resolution using Discrete Annotation (2020.acl-main)
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| 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. |
Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution (2023.acl-long)
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| Challenge: | Recent results show that annotating mentions is twice as fast as annotation of full coreference chains. |
| Approach: | They propose a method for efficiently adapting coreference models using only mentions in the target domain without increasing annotator time. |
| Outcome: | The proposed method improves average F1 without increasing annotator time. |
Adapting Coreference Resolution Models through Active Learning (2022.acl-long)
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| Challenge: | Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. |
| Approach: | They investigate how to actively label coreference by sampling a small subset of data for annotators to label. |
| Outcome: | The proposed model can be more realistic when labeling spans within the same document than when annotating spans across documents. |
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing (2020.coling-main)
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| Challenge: | Existing studies of natural language labelling tasks have shown that crowd-sourced labels can be noisy. |
| Approach: | They split the aggregation into mention classification and coreference chain inference tasks to predict the correct labels. |
| Outcome: | The proposed model predicts the class of each mention using an autoencoder while taking into account the mention’s annotation complexity and annotators’ reliability at different levels. |
Seq2seq is All You Need for Coreference Resolution (2023.emnlp-main)
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| Challenge: | Existing work on coreference resolution suggests task-specific models are necessary . a recent line of work that take an alternative approach leveraging advances in seq2seq-based models is needed . |
| Approach: | They propose a pretrained seq2seq transformer to map an input document to a tagged sequence encoding the coreference annotation. |
| Outcome: | The proposed model outperforms or matches the best coreference systems on an array of datasets. |
Neural Mention Detection (2020.lrec-1)
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| Challenge: | Mention detection is an important preprocessing step for downstream applications such as NER and coreference resolution. |
| Approach: | They propose and compare three approaches to mention detection using ELMO embeddings and a biaffine classifier. |
| Outcome: | The proposed model outperforms state-of-the-art models on the GENIA corpora and improves on mention recall. |
Coreference Resolution without Span Representations (2021.acl-short)
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| Challenge: | Pretraining has reduced many complex task-specific NLP models to simple lightweight layers. |
| Approach: | They propose a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, pruning heuristics, and more. |
| Outcome: | The proposed model performs competitively with the current standard model, while being simpler and more efficient. |
Model-based Annotation of Coreference (2020.lrec-1)
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| Challenge: | Annotators are asked to annotate coreferent spans of text, which is unnatural . we present an alternative in which annotators can preprocess documents and assign pronouns to entities. |
| Approach: | They propose an alternative in which annotators are asked to assign pronouns to entities and preprocess documents to create a knowledge base. |
| Outcome: | The proposed model-based approach leads to faster annotation and higher inter-annotator agreement and opens up an alternative approach to coreference resolution. |
Multitask Learning-Based Neural Bridging Reference Resolution (2020.coling-main)
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| Challenge: | Existing models for bridging references lack large corpora annotated with briding references . second challenge is different definitions of bridding used in different corpors . |
| Approach: | They propose a multi-task learning-based neural model for bridging reference resolution . they show substantial improvements of up to 8 p.p. on full briding resolution compared to previous models . |
| Outcome: | The proposed model outperforms the best reported results on full bridging resolution by up to 8 p.p. |