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. |
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Knowledge-aware Pronoun Coreference Resolution (P19-1)
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| 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. |
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. |
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. |
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. |
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. |
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. |
Exophoric Pronoun Resolution in Dialogues with Topic Regularization (2021.emnlp-main)
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| Challenge: | Existing studies on pronoun coreference resolution focus on anaphora and cataphores . exophoric pronounos are common in daily communications, but can be disambiguated by general topics of the dialogue. |
| Approach: | They propose to leverage local context and global topics of dialogues to solve out-of-text PCR problem by adding topic regularization. |
| Outcome: | Extensive experiments show that topic regularization can be used to solve the out-of-text PCR problem. |
Joint Coreference Resolution and Character Linking for Multiparty Conversation (2021.eacl-main)
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| Challenge: | Character linking is the task of linking mentioned people in conversations to the real world . human use of pronouns or normal entities makes it difficult to link mentioned people to real people . a critical step towards understanding conversations is grounding mentioned people - a goal of the natural language processing community . |
| Approach: | They propose to integrate richer context from the coreference relations among different mentions to help the linking task. |
| Outcome: | The proposed model outperforms all previous models on both tasks. |
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. |
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. |