| Challenge: | Existing work on identifying implicit arguments has been limited due to large number of features and small datasets . a neural model that uses narrative coherence and entity salience is used to train implicit arguments . |
| Approach: | They propose to train models for implicit argument prediction on a simple cloze task . they use narrative coherence and entity salience to build a neural model . |
| Outcome: | The proposed model performs better on synthetic and natural data. |
Similar Papers
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger. |
| Approach: | They propose a framework to extract event-related arguments from a given event frame-level scope. |
| Outcome: | The proposed method achieves state-of-the-art on the RAMS dataset. |
A Two-Step Approach for Implicit Event Argument Detection (2020.acl-main)
Copied to clipboard
| Challenge: | et al., 2015) only consider local arguments in the same sentence of the event trigger. |
| Approach: | They propose to decompose the implicit event argument detection task into two sub-problems . they propose to use argument head-word detection and head-to-span expansion to reduce the number of candidates. |
| Outcome: | The proposed model achieves better performance than a strong sequence labeling baseline. |
IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments (2022.lrec-1)
Copied to clipboard
| Challenge: | Using crowdsourcing, we show that models trained with domain-specific implicit reasonings outperform domain-general models in both automatic and human evaluations. |
| Approach: | They propose to create a domain-specific corpus of implicit reasonings annotated for a wide range of arguments and use it to generate models. |
| Outcome: | The proposed corpus outperforms domain-general models in automatic and human evaluations. |
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants (N18-1)
Copied to clipboard
| Challenge: | Existing methods for analyzing warrants in natural language arguments are insufficient. |
| Approach: | They propose a method for reconstructing warrants from news comments . they use a crowdsourcing process to obtain warrants for 2k authentic arguments . |
| Outcome: | The proposed method will define a substantial step towards automatic warrant reconstruction. |
Uncovering Implicit Inferences for Improved Relational Argument Mining (2023.eacl-main)
Copied to clipboard
| Challenge: | Argument mining attempts to extract arguments and their structure from unstructured texts. |
| Approach: | They propose a generative neuro-symbolic approach to finding inference chains that connect argument pairs by using the Commonsense Transformer. |
| Outcome: | The proposed approach outperforms the state-of-the-art by 2-5% in F1 score on three datasets. |
Dynamic Global Memory for Document-level Argument Extraction (2022.acl-long)
Copied to clipboard
| Challenge: | Recent work on document-level event argument extraction is restricted by sequence length constraints and ignores global context between events. |
| Approach: | They propose to construct a document memory store to extract contextual event information and leverage it to implicitly and explicitly help with decoding of arguments for later events. |
| Outcome: | The proposed framework outperforms prior methods and is more robust to adversarially annotated examples with constrained decoding design. |
Document-Level Event Argument Extraction by Conditional Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing event extraction models have been limited to the sentence level . this formulation signifies a misalignment between the information seeking behavior and the informative seeking behavior. |
| Approach: | They propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. |
| Outcome: | The proposed model achieves 7.6% F1 and 5.7% F1 over the best baseline on the document-level event extraction dataset WikiEvents and 9.3% F1 on the informative argument extraction task. |
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on event-specific argument extraction is limited to contiguous spans of text . Existing approaches to event-centric information extraction are limited to explicit arguments . |
| Approach: | They propose two key argument types that cannot be modeled by existing EE frameworks . implicit and scattered arguments are crucial to elicit full breadth of information required for proper event modeling. |
| Outcome: | The proposed dataset includes 7,464 argument annotations from online health discourse. |
Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)
Copied to clipboard
| Challenge: | Existing methods to extract event data are laborious to create and limited in size. |
| Approach: | They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. |
| Outcome: | The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods. |
Lexicosyntactic Inference in Neural Models (D18-1)
Copied to clipboard
| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
| Approach: | They build a factuality judgment dataset for English clause-embedding verbs in various syntactic contexts and use it to probe the behavior of current state-of-the-art neural systems. |
| Outcome: | The proposed model makes systematic errors that are visible through the lens of factuality prediction. |