About

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

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Latest papers without code

"Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays

8 Mar 2021

Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task.

ARGUMENT MINING GENERAL CLASSIFICATION MULTI-TASK LEARNING

Contextual Argument Component Classification for Class Discussions

20 Feb 2021

Argument mining systems often consider contextual information, i. e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction.

COMPONENT CLASSIFICATION GENERAL CLASSIFICATION RELATION EXTRACTION

Focusing Knowledge-based Graph Argument Mining via Topic Modeling

3 Feb 2021

We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence.

ARGUMENT MINING DECISION MAKING WORD EMBEDDINGS

Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation

26 Nov 2020

Argument Mining is defined as the task of automatically identifying and extracting argumentative components (e. g., premises, claims, etc.)

ARGUMENT MINING

Multilingual Argument Mining: Datasets and Analysis

13 Oct 2020

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets.

ARGUMENT MINING MACHINE TRANSLATION NATURAL LANGUAGE UNDERSTANDING TRANSFER LEARNING

Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization

ACL 2020

Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges.

ARGUMENT MINING

Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs

8 Jun 2020

This paper targets the automated extraction of components of argumentative information and their relations from natural language text.

ARGUMENT MINING

The Discussion Tracker Corpus of Collaborative Argumentation

LREC 2020

Although Natural Language Processing (NLP) research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals.

ARGUMENT MINING MULTI-TASK LEARNING

Corpus for Modeling User Interactions in Online Persuasive Discussions

LREC 2020

To analyze persuasive strategies, it is important to understand how individuals construct posts and comments based on the semantics of the argumentative components.

ARGUMENT MINING