Argument Mining
82 papers with code • 1 benchmarks • 6 datasets
Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
Source: AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
Benchmarks
These leaderboards are used to track progress in Argument Mining
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UKP
DebateSum
Subtasks
Most implemented papers
A Bayesian Approach for Sequence Tagging with Crowds
Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data.
Fine-Grained Argument Unit Recognition and Classification
trtm/AURC
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In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling.
A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd
The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality.
TARGER: Neural Argument Mining at Your Fingertips
achernodub/targer
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ACL 2019
We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus.
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
tuhinjubcse/AMPERSAND-EMNLP2019
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IJCNLP 2019
Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory.
Extracting Implicitly Asserted Propositions in Argumentation
yohanjo/emnlp20_prop_extr
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EMNLP 2020
Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.
Aspect-Based Argument Mining
In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS).
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content
LCS2-IIITD/LESA-EACL-2021
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EACL 2021
We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset.
Multi-Task Attentive Residual Networks for Argument Mining
AGalassi/StructurePrediction18
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We explore the use of residual networks and neural attention for multiple argument mining tasks.
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Data exploration is an important step of every data science and machine learning project, including those involving textual data.
