Lin: Unsupervised Extraction of Tasks from Textual Communication (2020.coling-main)
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| Challenge: | Identifying tasks from emails and chats is a hallmark of collaborative communication . state-of-the-art approaches for task identification rely on large annotated datasets . |
| Approach: | They propose an unsupervised approach to identifying tasks that leverages dependency parsing and VerbNet. |
| Outcome: | The proposed approach yields comparable or more accurate results than supervised models on unseen domains. |
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| Challenge: | Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets. |
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UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)
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| Challenge: | Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask. |
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GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction (2025.emnlp-demos)
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| Challenge: | Existing solutions for information extraction (IE) require specialized models for different tasks or require expensive large language models. |
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Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)
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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang
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Learning Cross-Task Dependencies for Joint Extraction of Entities, Events, Event Arguments, and Relations (2022.emnlp-main)
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| Challenge: | Existing work on IE tasks that use two types of dependencies is not optimal . emr, event trigger detection, event argument extraction, and relation extraction are challenging . |
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Learning to Decompose and Organize Complex Tasks (2021.naacl-main)
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| Challenge: | Using a novel end-to-end pipeline, we propose a solution that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
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tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework (2024.lrec-main)
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| Challenge: | Several initiatives release harmonized datasets or provide harmonization codes to preprocess datasets into a consistent format. |
| Approach: | They propose an annotation framework that enables concise, readable, and reusable annotations. |
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Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2021.eacl-main)
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| Challenge: | Existing approaches to learning from examples are limited due to the vast number of languages, domains and tasks. |
| Approach: | They propose a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. |
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Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction (D19-62)
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| Challenge: | 80% of the data sets for relation extraction tasks are negative instances, resulting in a lack of syntactic information between two entity mentions. |
| Approach: | They propose a graph convolutional networks model that incorporates dependency parsing and contextualized embedding to capture comprehensive contextual information. |
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