---
title: "A Context-Dependent Gated Module for — Lai 2021"
description: "The paper addresses within-document event coreference resolution: grouping event mentions in a text that describe the same real-world event."
authors: ["Tuan Lai", "Heng Ji", "Trung Bui", "Quan Hung Tran", "Franck Dernoncourt", "Walter Chang"]
year: 2021
venue: "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/2021.naacl-main.274/"
doi: "10.18653/v1/2021.naacl-main.274"
arxiv: "2104.01697"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/lai-2021-context-dependent-gated-module-incorporating/"
---
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# A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution

**Authors:** Tuan Lai, [Heng Ji](https://md.research.elamur.ai/free/authors/heng-ji/), [Trung Bui](https://md.research.elamur.ai/free/authors/trung-bui/), [Quan Hung Tran](https://md.research.elamur.ai/free/authors/quan-hung-tran/), [Franck Dernoncourt](https://md.research.elamur.ai/free/authors/franck-dernoncourt/), [Walter Chang](https://md.research.elamur.ai/free/authors/walter-chang/) · **Year:** 2021 · **Venue:** Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.naacl-main.274/) · [PDF](https://aclanthology.org/2021.naacl-main.274.pdf) · [DOI 10.18653/v1/2021.naacl-main.274](https://doi.org/10.18653/v1/2021.naacl-main.274) · [arXiv 2104.01697](https://arxiv.org/abs/2104.01697) · [OpenAlex](https://openalex.org/W3171289259)

**Citations:** 27 citations (openalex-snapshot · 2026-10-14) · [21](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses within-document event coreference resolution: grouping event mentions in a text that describe the same real-world event. The authors argue that strong pretrained encoders such as SpanBERT still benefit from symbolic features like event type, modality, polarity, genericity, tense and realis. However, upstream systems predict these features automatically, so they are often wrong, and their usefulness depends on context. The authors propose a Context-Dependent Gated Module (CDGM). It splits each feature-based pair representation into two parts relative to the trigger-based representation: a part parallel to it (redundant) and a part orthogonal to it (new). A sigmoid gate then decides how to mix the two. To teach the gates to handle unreliable inputs, they add a noisy training scheme that randomly swaps feature values during training. On ACE 2005 and KBP 2016, with predicted mentions and features, the full model clearly beats a SpanBERT baseline and naive feature concatenation. It surpasses prior systems on KBP 2016. The work shows a general way to use imperfect symbolic signals alongside contextual embeddings.

## Key points
- **Features and tools:** ACE 2005 features are type, polarity, modality, genericity and tense; KBP 2016 features are type and realis. Event mentions and subtypes come from OneIE, and the remaining attributes come from a SpanBERT-based joint classifier. Feature accuracy on the test sets ranges from about 0.76 (tense) to 0.99 (polarity).
- **ACE 2005, end-to-end:** CDGM + Noise reaches 62.07 CoNLL and 59.76 AVG. The baseline scores 58.93 / 55.78, and simple concatenation scores 57.55 / 54.79, slightly below the baseline. A multi-task alternative reaches only 56.5 AVG. These ACE scores are not directly comparable with earlier work, which used 10-fold cross-validation.
- **KBP 2016, end-to-end:** the full model scores 43.55 CoNLL and 40.61 AVG, ahead of E3C (41.97 / 38.66) and the baseline (40.57 / 37.59).
- **Ablations:** the gains over concatenation are largest for noisier features, such as modality (+3.73 AVG) and genericity (+3.12 AVG). With ground-truth features, CDGM alone works best (88.30 AVG), and noisy training no longer helps.
- **Code:** publicly available at github.com/laituan245/eventcoref.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cited by

Cited by

21 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 18 not yet.

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/papers/pan-2023-unifying-large-language-models-knowledge/)
- [What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://md.research.elamur.ai/free/papers/ravi-2023-what-happens-before-after-multi/)
- [Improving Event Coreference Resolution Using Document-level and Topic-level Information](https://md.research.elamur.ai/free/papers/xu-2022-improving-event-coreference-resolution-document/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{lai2021context,
  title = {{A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution}},
  author = {Lai, Tuan and Ji, Heng and Bui, Trung and Tran, Quan Hung and Dernoncourt, Franck and Chang, Walter},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.274},
  eprint = {2104.01697},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.274}
}
```

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30. Adam Paszke, S. Gross, Francisco Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, Alban Desmaison, Andreas Köpf, E. Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, B. Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. Pytorch: An im… [find](https://scholar.google.com/scholar?q=Adam+Paszke%2C+S%2E+Gross%2C+Francisco+Massa%2C+A%2E+Lerer%2C+J%2E+Bradbury%2C+G%2E+Chanan%2C+T%2E+Killeen%2C+Z%2E+Lin%2C+N%2E+Gimelshein%2C+L%2E+Antiga%2C+Alban+Desmaison%2C+Andreas+K%C3%B6pf%2C+E%2E+Yang%2C+Zach+DeVito%2C+Martin+Raison%2C+Alykhan+Teja) [our card](https://md.research.elamur.ai/free/papers/paszke-2019-pytorch-imperative-style-high-performance/)
31. Haoruo Peng, Yangqiu Song, and Dan Roth. 2016. Event detection and co-reference with minimal supervision. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 392–402, Austin, Texas. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Haoruo+Peng%2C+Yangqiu+Song%2C+and+Dan+Roth%2E+2016%2E+Event+detection+and+co%2Dreference+with+minimal+supervision%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pag) [our card](https://md.research.elamur.ai/free/papers/peng-2016-event-detection-co-reference-minimal/)
32. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+Manning%2E+2014%2E+GloVe%3A+Global+vectors+for+word+representation%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Process)
33. M. Recasens, E. Hovy, and M. Martí. 2011. Identity, non-identity, and near-identity: Addressing the complexity of coreference. Lingua, 121:1138–1152. [find](https://scholar.google.com/scholar?q=M%2E+Recasens%2C+E%2E+Hovy%2C+and+M%2E+Mart%C3%AD%2E+2011%2E+Identity%2C+non%2Didentity%2C+and+near%2Didentity%3A+Addressing+the+complexity+of+coreference%2E+Lingua%2C+121%3A1138%E2%80%931152%2E)
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38. Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006. [find](https://scholar.google.com/scholar?q=Christopher+Walker%2C+Stephanie+Strassel%2C+Julie+Medero%2C+and+Kazuaki+Maeda%2E+2006%2E)
39. Ace 2005 multilingual training corpus. Linguistic Data Consortium, Philadelphia, 57:45. [find](https://scholar.google.com/scholar?q=Ace+2005+multilingual+training+corpus%2E+Linguistic+Data+Consortium%2C+Philadelphia%2C+57%3A45%2E)
40. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Remi Louf, Morgan Funtow-icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lh… [find](https://scholar.google.com/scholar?q=Thomas+Wolf%2C+Lysandre+Debut%2C+Victor+Sanh%2C+Julien+Chaumond%2C+Clement+Delangue%2C+Anthony+Moi%2C+Pier%2Dric+Cistac%2C+Tim+Rault%2C+Remi+Louf%2C+Morgan+Funtow%2Dicz%2C+Joe+Davison%2C+Sam+Shleifer%2C+Patrick+von+Platen%2C+Clara) [our card](https://md.research.elamur.ai/free/papers/wolf-2020-transformers-state-art-natural-language/)
41. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Remi Louf, Morgan Funtow-icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lh… [find](https://scholar.google.com/scholar?q=Thomas+Wolf%2C+Lysandre+Debut%2C+Victor+Sanh%2C+Julien+Chaumond%2C+Clement+Delangue%2C+Anthony+Moi%2C+Pier%2Dric+Cistac%2C+Tim+Rault%2C+Remi+Louf%2C+Morgan+Funtow%2Dicz%2C+Joe+Davison%2C+Sam+Shleifer%2C+Patrick+von+Platen%2C+Clara) [our card](https://md.research.elamur.ai/free/papers/wolf-2020-transformers-state-art-natural-language/)
42. Train (\# Docs) 529 509 Dev (\# Docs) 30 139 Test (\# Docs) 40 169 *Table 9: Basic statistics of the datasets.* A.1 Symbolic Feature Predictors [find](https://scholar.google.com/scholar?q=Train+%28%5C%23+Docs%29+529+509+Dev+%28%5C%23+Docs%29+30+139+Test+%28%5C%23+Docs%29+40+169+%2ATable+9%3A+Basic+statistics+of+the+datasets%2E%2A+A%2E1+Symbolic+Feature+Predictors)