Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking (P18-2)
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| Challenge: | a cross-lingual coreference model is based on multi-lingual embeddings and language independent features. |
| Approach: | They propose a crosslingual coreference model that builds on multi-lingual embeddings and language independent features. |
| Outcome: | The proposed model outperforms the existing models on Chinese and Spanish test sets. |
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| Challenge: | Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. |
| Approach: | They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures. |
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Parallel Data Helps Neural Entity Coreference Resolution (2023.findings-acl)
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| Challenge: | Current neural coreference models are trained on monolingual annotated data but annotating such coreference information is expensive and challenging. |
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Multi-lingual Entity Discovery and Linking (P18-5)
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
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What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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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 . |
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Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities (2022.naacl-main)
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| Challenge: | Identifying related entities and events within and across documents is fundamental to natural language understanding. |
| Approach: | They propose an approach to entity and event coreference resolution using contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art results on key metrics on the ECB+ corpus and is competitive on others. |
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)
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| 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) |
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Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks (2021.naacl-main)
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| Challenge: | Masked language models have become the de facto standard when processing text . however, these models are evaluated in a monolingual setting only . |
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Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)
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| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
| Approach: | They propose a task that uses multilingual BERT representations of both the mention and context as input and explore zero-shot language transfer. |
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Contextualized End-to-End Neural Entity Linking (2020.aacl-main)
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| Challenge: | a proposed entity linking model that disjointly applies MD and ED from the same contextualized BERT embeddings is able to generalize better. |
| Approach: | They propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED) they propose to use task-specific heads on top of shared BERT contextualized embeddings to learn MD and ED. |
| Outcome: | The proposed model achieves state-of-the-art results across a standard EL dataset and under a setting where hand-crafted candidate sets are not available. |