Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset (N18-2)
Copied to clipboard
| Challenge: | Argument mining is a subset of NLP that deals with extracting arguments from user-based content. |
| Approach: | They propose to use weakly supervised and semi-supervised methods to automatically annotate reviews and provide large annotated datasets. |
| Outcome: | The proposed methods can be used to learn better models for implicit/explicit opinion classification. |
Similar Papers
-Stance: A Large-Scale Real World Dataset of Stances in Legal Argumentation (2025.acl-long)
Copied to clipboard
| Challenge: | Current tools for legal argument reasoning do not support this task. |
| Approach: | They propose to use a large-scale dataset to facilitate work on the legal argument stance classification task by evaluating whether a case summary strengthens or weakens a legal argument. |
| Outcome: | The proposed dataset is used to facilitate work on the legal argument stance classification task, which involves assessing whether a case summary strengthens or weakens a legal argument (polarity) and to what extent (intensity). |
Can We Identify Stance without Target Arguments? A Study for Rumour Stance Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing target-aware models underperform in cases where the context of the target is crucial. |
| Approach: | They propose a framework to enhance reasoning with the targets and propose 'target-aware' models without awareness of the target. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark datasets. |
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
Copied to clipboard
Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
Sentiment-Stance-Specificity (SSS) Dataset: Identifying Support-based Entailment among Opinions. (L18-1)
Copied to clipboard
| Challenge: | Argument mining is a method for extracting argument components and structures from natural language texts. |
| Approach: | They propose to model arguments as a set of premises that either support each other or collectively support a conclusion. |
| Outcome: | The proposed rules give an overall accuracy of 0.83 for the three datasets. |
Unsupervised Argumentation Mining in Student Essays (2020.lrec-1)
Copied to clipboard
| 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. |
Argument Mining for Review Helpfulness Prediction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Argumentational features have been shown to be promising indicators of product review helpfulness, but their utility has been limited due to the lack of resources and large-scale experiments investigating their utility. |
| Approach: | They present an argumentational argumentation model that annotates 878 Amazon reviews on headphones and uses it to evaluate argument quality. |
| Outcome: | The proposed model improves the state-of-the-art model under text-only and text-and-image settings. |
Human Rationales as Attribution Priors for Explainable Stance Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. |
| Approach: | They propose a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. |
| Outcome: | The proposed method improves the reasoning of a state-of-the-art classifier in a data-scarce setting at no cost in predictive performance. |
STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval (2020.findings-emnlp)
Copied to clipboard
Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
| Challenge: | a new news dataset targets both stance detection (SD) and fine-grained evidence retrieval (ER) . stance Detection (SD), which is a form of multitask learning, has gained increasing interest in recent work . |
| Approach: | They propose a news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER) their dataset is an expert-annotated news dataset with 3,291 articles. |
| Outcome: | The proposed dataset is a high-quality benchmark for future research in stance detection and evidence retrieval. |
Towards an argumentative content search engine using weak supervision (C18-1)
Copied to clipboard
| Challenge: | Existing work focused on detecting claims within a small set of documents . however, pinpointing relevant claims within massive unstructured corpora, received little attention. |
| Approach: | They propose to use a weak signal to develop a query for claim–sentence detection using a large text corpus. |
| Outcome: | The proposed system outperforms previous results in terms of precision and coverage. |
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
Copied to clipboard
Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |