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Found 18 papers, 0 papers with code
The workweek is the best time to start a family -- A Study of GPT-2 Based Claim Generation
Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information.
Multilingual Argument Mining: Datasets and Analysis
The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets.
What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus
One of the most impressive human endeavors of the past two decades is the collection and categorization of human knowledge in the free and accessible format that is Wikipedia.
Out of the Echo Chamber: Detecting Countering Debate Speeches
An educated and informed consumption of media content has become a challenge in modern times.
Financial Event Extraction Using Wikipedia-Based Weak Supervision
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.
Corpus Wide Argument Mining -- a Working Solution
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation
Recent advancements in machine reading and listening comprehension involve the annotation of long texts.
A Dataset of General-Purpose Rebuttal
In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog.
Argument Invention from First Principles
In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.
Controversy in Context
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is.
Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining
To this end, we collected a large dataset of $400$ speeches in English discussing $200$ controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech.
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion
When debating a controversial topic, it is often desirable to expand the boundaries of discussion.
Listening Comprehension over Argumentative Content
We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset.
Automatic Speech Recognition (ASR)
Machine Reading Comprehension
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Computational Argumentation Quality Assessment in Natural Language
Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation.
