Challenge: Existing methods for extracting life events from conversations are limited.
Approach: They propose a dataset containing fine-grained life event annotations on conversational data.
Outcome: The proposed dataset combines three information extraction frameworks to extract life events from conversations.

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Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)

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Challenge: Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature.
Approach: They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach.
Outcome: The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data.
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)

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Challenge: Existing approaches to event extraction are limited to a set of pre-defined types.
Approach: They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text.
Outcome: The proposed framework outperforms existing methods on zero-shot event extraction.
DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)

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Challenge: Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets.
Approach: They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments.
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Dialogue-Based Relation Extraction (2020.acl-main)

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Challenge: Existing dialogue-based relation extraction tasks focus on texts from formal genres such as professionally written and edited news reports or well-edited websites.
Approach: They propose to use DialogRE to study cross-sentence relation extraction . they propose to annotate 36 possible relation types between arguments in dialogues .
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EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems (2026.acl-long)

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Challenge: Existing dialogue systems process conversational turns in isolation, overlooking event structures that guide natural interactions.
Approach: They propose a framework that explicitly models relationships between conversational events to generate more contextually appropriate dialogue responses.
Outcome: Experiments on three dialogue datasets show that the proposed approach produces more natural responses while requiring less computational overhead.
MEE: A Novel Multilingual Event Extraction Dataset (2022.emnlp-main)

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Challenge: Existing methods for Event Extraction are limited for non-English languages . lack of high-quality multilingual datasets has been the main hindrance .
Approach: They propose a multilingual event extraction dataset that provides annotation for more than 50K event mentions in 8 typologically different languages.
Outcome: The proposed dataset provides annotation for more than 50K event mentions in 8 languages . the proposed dataset will be publicly available to foster future research .
MailEx: Email Event and Argument Extraction (2023.emnlp-main)

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Challenge: Existing work on email event extraction only covers one specific aspect of email information and cannot connect with other relevant tasks.
Approach: They propose a new taxonomy for performing event extraction from conversational email threads.
Outcome: The proposed taxonomy covers 10 event types and 76 arguments in the email domain.
Event Extraction by Answering (Almost) Natural Questions (2020.emnlp-main)

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Challenge: Existing work in event argument extraction relies heavily on entity recognition as a preprocessing/concurrent step, causing error propagation.
Approach: They propose a question answering task that extracts event arguments in an end-to-end manner.
Outcome: The proposed framework outperforms prior work on the ACE 2005 task on event argument extraction.
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)

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Challenge: Recent studies suggest that event extraction evaluations may not accurately reflect the true performance.
Approach: They propose a standardized, fair, and reproducible benchmark for event extraction . they use standardized scripts and splits for 16 datasets spanning eight domains .
Outcome: The proposed benchmarks show that they struggle to achieve satisfactory performance.
Rapid Customization for Event Extraction (P19-3)

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Challenge: a novel system allows users to customize event extraction to find new event types and their arguments.
Approach: They propose a system that allows a user to find, expand and filter event triggers by exploring an unannotated development corpus.
Outcome: The proposed system can find, expand and filter event triggers from an unannotated development corpus . it trains a generic argument attachment model for extracting Actor, Place, and Time .

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