| Challenge: | Existing models for pronoun coreference resolution only use triplets, the most common format for knowledge graphs. |
| Approach: | They propose a model that leverages different types of knowledge to resolve pronoun coreference with a neural model. |
| Outcome: | The proposed model outperforms state-of-the-art baselines on two datasets from different domains. |
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Incorporating Context and External Knowledge for Pronoun Coreference Resolution (N19-1)
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| Challenge: | Existing models for pronoun coreference resolution rely on manual definitions and features to resolve pronounous coreferences. |
| Approach: | They propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models by a large margin. |
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (D19-1)
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| Challenge: | Large-scale training sets for pronoun resolution are scarce, since manually labelling data is costly. |
| Approach: | They propose a language-model-based approach to solve pronoun disambiguation problems using a WikiCREM dataset. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on 6 out of 7 datasets. |
Incorporating Centering Theory into Neural Coreference Resolution (2022.naacl-main)
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| Challenge: | Recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. |
| Approach: | They propose to incorporate centering transitions derived from centering theory into a neural coreference model by using a graph. |
| Outcome: | The proposed model improves on pronoun resolution in long documents, formal well-structured text, and clusters with scattered mentions. |
PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English (2022.lrec-1)
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| Challenge: | Existing PCR systems are not reliable in real applications due to weak semantic meanings of pronouns. |
| Approach: | They propose a benchmark and toolbox that evaluates performance of PCR systems from different perspectives. |
| Outcome: | The proposed benchmark and toolbox evaluates the performance of PCR systems from different perspectives. |
The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution (P19-1)
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| Challenge: | Existing methods for coreference resolution exploit the number and gender of antecedents or have been handcrafted and do not reflect the diversity of naturally occurring text. |
| Approach: | They propose a trick to improve resolution by antecedent switching to target common-sense understanding and world knowledge. |
| Outcome: | The proposed method achieves state-of-the-art results on the GAP coreference task. |
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation (2020.coling-main)
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| Challenge: | Recent high scores on pronoun translation suggest current approaches work well . et al., 2018: are context-aware nmt models learning this task? |
| Approach: | They propose a test set to assess the ability to handle specific steps for pronoun translation . they propose heuristics that break down when translations require real reasoning . |
| Outcome: | The proposed model can model complex inferences required for translation of english into german . it shows that current approaches are not able to model all of this information well . |
What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues (D19-1)
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| Challenge: | a core task of natural language understanding is to ground a pronoun to a visual object it refers to . problem arises when people use pronounos to refer to something they can see without prior introduction . a novel visual-aware PCR model is proposed to solve this problem . |
| Approach: | They propose a visual-aware PCR model to ground a pronoun to a visible object . they propose PCR using a large-scale dialogue dataset to investigate this problem . |
| Outcome: | The proposed model can help resolve pronouns in conversational contexts. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
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| Challenge: | Existing models for coreference resolution are based on independent mention pair-wise decisions. |
| Approach: | They propose a model that learns coreference at the document-level and takes global decisions. |
| Outcome: | The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution. |
Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers (D18-1)
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| Challenge: | Recent coreference resolvers have notable improvements on the CoNLL evaluation sets, but struggle to generalize properly to new datasets. |
| Approach: | They investigate the role of linguistic features in building more generalizable coreference resolvers . they show that employing features and subsets of their values that are informative for coreference resolution improves generalization . |
| Outcome: | The proposed system achieves state-of-the-art results on WikiCoref, compared with a system trained on CoNLL. |
Zero Pronoun Resolution with Attention-based Neural Network (C18-1)
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| Challenge: | Recent neural network methods for zero pronoun resolution use contextual information to encode the zero pronomins since they contain no actual content. |
| Approach: | They propose a self-attention mechanism for encoding zero pronouns that focus on some informative parts of the associated texts and produce an efficient way of encode them. |
| Outcome: | The proposed model significantly surpasses existing Chinese zero pronoun resolution baseline systems. |