Multi-Target Stance Detection via a Dynamic Memory-Augmented Network

Wei, Penghui; Lin, Junjie; Mao, Wenji

doi:10.1145/3209978.3210145

Cited by 39 publications

(39 citation statements)

References 3 publications

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“…Algorithm Metric Score Zarella and Marsh [69] Favor/against/neither RNN F1 0.68 Mohammad et al [68] Favor/against/neither linear-kernel SVM F-micro 0.70 F-macro 0.59 Wei et al [98] Favor/against/neither Neural network F1 0.56 Wei et al [70] Favor/against Neural network F1 0.71 Ebrahimi et al [71] Favor/against/neither Linear-kernel SVM F macro 0.57 Johnson and Goldwasser [73] Favor/against Probabilistic Soft Logic A 0.86 Lai et al [72] Favor/against SVM F-macro 0.90 Addawood et al [60] Favor/against/neutral SVM P 0.90 R 0.90 F1 0.90 Table 6: The results of the supervised and weakly-supervised ML approaches that have been followed for the stance detection in social media text. Fully supervision on [69], [68], [60].…”

Section: # Classesmentioning

confidence: 99%

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The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

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Section: # Classesmentioning

confidence: 99%

“…Fully supervision on [69], [68], [60]. Weakly supervision on [70], [71], [73], [72]. The [69], [68], [98], [71] and [60] are applied on the same dataset.…”

Section: # Classesmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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show abstract

“…By considering the dependency of related targets, Sobhani et al [5] introduced a multi-target stance detection (MTSD) task and proposed an attentive encoder-decoder network to capture the dependencies among stance labels regarding multiple targets. Later, Wei et al [23] proposed a dynamic memory-augmented network that utilized a shared external memory to capture and store multi-targets stance indicative clues dynamically.…”

Section: Related Workmentioning

confidence: 99%

“…We compared the performance of our proposed method (MKC-LSTMVs-ATT) with the state-of-the-art multi-target stance detection methods including Seq2Seq method proposed by Sobhani et al [50] and DMAN method proposed by Wei et al [23]. The comparative results are presented in Table 7.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…The encoder converted the input text into a vector representation, and the decoder generates stance labels towards multiple targets. DMAN [23] utilized the dynamic memory augmented network to capture and store stance-indicative information for related targets. Results showed that our MKC-LSTMVs-ATT method substantially surpassed these methods and gained 3.91% and 1.99% improvement over the Seq2Seq and DMAN methods, respectively.…”

Section: Comparison With Related Workmentioning

confidence: 99%

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Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Siddiqua

Chy

Aono

2019

IEICE Trans. Inf. & Syst.

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Umme Aymun SIDDIQUA †a) , Abu Nowshed CHY †b) , Nonmembers, and Masaki AONO †c) , Member SUMMARY Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. Detecting and analyzing user stances from massive opinion-oriented twitter posts provide enormous opportunities to journalists, governments, companies, and other organizations. Most of the prior studies have explored the traditional deep learning models, e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) for detecting stance in tweets. However, compared to these traditional approaches, recently proposed densely connected bidirectional LSTM and nested LSTMs architectures effectively address the vanishinggradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural network model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higherlevel tweet representations. Results of extensive experiments on single and multi-target benchmark stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.

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Stance Detection in Web and Social Media: A Comparative Study

Ghosh

Singhania

Singh

et al. 2019

Lecture Notes in Computer Science

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Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets-(i) the popular SemEval microblog dataset, and (ii) a set of health-related online news articles-we also perform a detailed comparative analysis of various methods and explore their shortcomings.

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Multi-Target Stance Detection via a Dynamic Memory-Augmented Network

Cited by 39 publications

References 3 publications

The evolution of argumentation mining: From models to social media and emerging tools

The evolution of argumentation mining: From models to social media and emerging tools

Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Stance Detection in Web and Social Media: A Comparative Study

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