The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants (N18-1)
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| 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. |
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Probing Neural Network Comprehension of Natural Language Arguments (P19-1)
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| Challenge: | Argument Reasoning Comprehension Task (ARCT) focuses on inferences, not just discovering warrants. |
| Approach: | They propose to build an adversarial dataset on which all models achieve random accuracy. |
| Outcome: | The proposed dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work. |
Extracting Implicitly Asserted Propositions in Argumentation (2020.emnlp-main)
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| Challenge: | Argumentation is a rhetorical device that asserts propositions implicitly, but few studies have examined the issue. |
| Approach: | They propose a computational method for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. |
| Outcome: | The proposed models are based on a corpus of 2016 debates and online commentary. |
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction (2021.emnlp-main)
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| Challenge: | Existing datasets are too small to train a model for capturing regularities underlying how event arguments are extracted. |
| Approach: | They propose to bridge implicit EAE with machine reading comprehension (MRC) by building a unified training framework and explicit data augmentation regimes via MRC. |
| Outcome: | The proposed method obtains state-of-the-art performance on two benchmarks and demonstrates superior results in a data-low scenario. |
Implicit Argument Prediction with Event Knowledge (N18-1)
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| 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. |
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)
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| 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. |
Transferring Confluent Knowledge to Argument Mining (2022.coling-1)
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| Challenge: | Argument mining is a natural language processing task that seeks to obtain structured arguments from unstructured text. |
| Approach: | They propose to use a transfer learning methodology to assess the potential of argument mining knowledge with confluent tasks. |
| Outcome: | The proposed method dispenses with heavy feature and model engineering and allows for new state-of-the-art performance for its three main sub-tasks. |
A Two-Step Approach for Implicit Event Argument Detection (2020.acl-main)
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| 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. |
Listening Comprehension over Argumentative Content (D18-1)
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Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
| Challenge: | In argumentation domain, people are exposed directly to audio (or the video), without access to a written version. |
| Approach: | They present a task for machine listening comprehension in the argumentation domain and a dataset in English. |
| Outcome: | The proposed task is based on 200 speeches arguing for or against 50 controversial topics and uses baseline methods to address it. |
Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments (2025.acl-long)
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| Challenge: | Identifying arguments is a prerequisite for various tasks in automated discourse analysis. |
| Approach: | They evaluate four BERT-like transformers on 17 English sentence-level datasets . they find that they tend to rely on lexical shortcuts tied to content words . |
| Outcome: | The proposed models perform best on 17 English sentence-level datasets on common tasks, but their performance drops when applied to unseen datasets. |
Unsupervised Argumentation Mining in Student Essays (2020.lrec-1)
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| Challenge: | State-of-the-art argumentation mining systems rely on annotated training data and are supervised, thus relying on an annotation of the components and relationships between them. |
| Approach: | They propose to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. |
| Outcome: | The proposed approach outperforms two supervised baselines and achieves 73.5-83.7% of the performance of a state-of-the-art neural approach. |