Constrained Multi-Task Learning for Event Coreference Resolution

Lü, Jing; Ng, Vincent

doi:10.18653/v1/2021.naacl-main.356

Cited by 16 publications

(9 citation statements)

References 19 publications

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“…In light of the above discussion, we present an empirical analysis of our state-of-the-art spanbased event coreference resolver (Lu and Ng, 2021) with the goal of gaining insights into its behavior. We believe that our analysis will not only provide the general NLP audience with a better understanding of the strengths and weaknesses of span-based event coreference models, but also provide coreference researchers with directions for future work.…”

Section: Introductionmentioning

confidence: 99%

Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art

Lü¹,

Ng²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Section: Introductionmentioning

confidence: 99%

Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art

Lü¹,

Ng²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…We compare our proposed CorefPrompt with the following strong baselines under the same evaluation settings: (i) the joint model Lu&Ng2021 (Lu and Ng, 2021b), which jointly models six related event and entity tasks, and (ii) the pairwise model Xu2022 (Xu et al, 2022), which introduces a document-level event encoding and event topic model. In addition, we build two pairwise baselines, BERT and RoBERTa, that utilize the popular BERT/RoBERTa model as the encoder.…”

Section: Resultsmentioning

confidence: 99%

CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities

Xu,

Li,

Zhu

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Event coreference resolution (ECR) aims to group event mentions referring to the same realworld event into clusters. Most previous studies adopt the "encoding first, then scoring" framework, making the coreference judgment rely on event encoding. Furthermore, current methods struggle to leverage human-summarized ECR rules, e.g., coreferential events should have the same event type, to guide the model. To address these two issues, we propose a prompt-based approach, CorefPrompt, to transform ECR into a cloze-style MLM (masked language model) task. This allows for simultaneous event modeling and coreference discrimination within a single template, with a fully shared context. In addition, we introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility, to explicitly demonstrate the reasoning process of ECR, which helps the model make final predictions. Experimental results show that our method CorefPrompt 1 performs well in a state-of-the-art (SOTA) benchmark.

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“…However, with the multi-output model, we can also identify the next group the user will be with 58% accuracy and the next activity the user will perform with 99% accuracy. Further optimization via advanced LSTM cells that contain task-specific parameters for task-specific learning can improve the model's performance [40].…”

Section: Modelmentioning

confidence: 99%

W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing

Atrey,

Zakaria,

Balan

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we propose W4-Groups that captures the functional perspective of variability and repeatability when automatically constructing short-term and long-term groups via multiple data sources (e.g., WiFi and location check-in data). We design and implement W4-Groups to detect and extract all four group features who-what-when-where from the user's daily mobility patterns. We empirically evaluate the framework using two real-world WiFi datasets and a location check-in dataset, yielding an average of 92% overall accuracy, 96% precision, and 94% recall. Further, we supplement two case studies to demonstrate the application of W4-Groups for next-group activity prediction and analyzing changes in group behavior at a longitudinal scale, exemplifying short-term and long-term occurrences.

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Constrained Multi-Task Learning for Event Coreference Resolution

Cited by 16 publications

References 19 publications

Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art

Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art

CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities

W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing

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