Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates (D18-1)
Copied to clipboard
| Challenge: | Existing models for agreement/disagreement in debates lack the ability to model these two factors together. |
| Approach: | They propose a hybrid attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. |
| Outcome: | The proposed model outperforms the state-of-the-art models on three (dis)agreement inference datasets. |
Similar Papers
Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for analyzing argumentative debates are insufficient to understand complex tasks. |
| Approach: | They propose a hybrid method to automatically predict the winning stance in argumentative debates using arguments from argumentation theory and semantics. |
| Outcome: | The proposed method is based on an unexplored new instance of the automatic analysis of natural language arguments. |
Leveraging Local and Global Patterns for Self-Attention Networks (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to integrate local and global information into self-attention networks have been criticized for overlooking neighboring information. |
| Approach: | They propose a hybrid attention mechanism to leverage local and global information . they use a gating scalar to integrate both sources of information based on local contexts . |
| Outcome: | The proposed approach improves on translation tasks and shows that the two types of contexts are complementary. |
Multi-Head Attention with Disagreement Regularization (D18-1)
Copied to clipboard
| Challenge: | Existing methods to encourage diversity among multi-head attention are limited. |
| Approach: | They propose a disagreement regularization term to encourage diversity among attention heads . they validated their approach on EnglishGerman and ChineseEnglish translation tasks . |
| Outcome: | The proposed approach improves translation performance across language pairs on English-German and Chinese-English translation tasks. |
Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for predicting a post's stance polarity and intensity don't take into account the stance's degree of intensity. |
| Approach: | They propose to use a new problem to train models for predicting a post's stance polarity and intensity in cyber argumentation. |
| Outcome: | The proposed models perform best for prediction of stance polarity with an accuracy of 70.43% and intensity with RMSE of 0.596. |
Is Attention Explanation? An Introduction to the Debate (2022.acl-long)
Copied to clipboard
Adrien Bibal, Rémi Cardon, David Alfter, Rodrigo Wilkens, Xiaoou Wang, Thomas François, Patrick Watrin
| Challenge: | Attention has been used in various tasks of NLP and other fields of machine learning to increase performance and provide some explanations. |
| Approach: | They propose to use attention as an explanation for deep learning models to increase performance . they propose to apply attention weights to queries and queries based on scalar scores . |
| Outcome: | The proposed model can be used to increase performance while providing some explanations. |
Multi-view Models for Political Ideology Detection of News Articles (D18-1)
Copied to clipboard
| Challenge: | Existing models for automatic detection of political ideology only leverage textual cues to identify the ideology evinced by a news article. |
| Approach: | They propose a novel attention based multi-view model that leverages cues from textual content and the network structure of news articles to identify political ideology. |
| Outcome: | The proposed model outperforms state of the art models by 10 percentage points on a battery of baselines and compares with baselines. |
PaLM: A Hybrid Parser and Language Model (D19-1)
Copied to clipboard
| Challenge: | Recent language models have shown strong data-fitting performance, but do not explicitly encode any notion of structural information. |
| Approach: | They propose a hybrid parser and neural language model that adds an attention layer over text spans in the left context. |
| Outcome: | The proposed model outperforms baseline models on language modeling and provides syntactically-informed representations of the context. |
Convolutional Interaction Network for Natural Language Inference (D18-1)
Copied to clipboard
| Challenge: | Attention-based neural models have achieved great success in natural language inference (NLI). |
| Approach: | They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. |
| Outcome: | The proposed model can capture complex interactions on three large datasets. |
Recognising Agreement and Disagreement between Stances with Reason Comparing Networks (P19-1)
Copied to clipboard
| Challenge: | Existing methods for (dis)agreement detection focus on conversational settings . however, non-dialogic stance-bearing utterances are common in real-world scenarios . |
| Approach: | They propose a reason comparing network to leverage reason information for stance comparison. |
| Outcome: | The proposed method outperforms baselines on a well-known stance corpus. |
Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games. |
| Approach: | They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation . |
| Outcome: | The proposed model predicts winning arguments in reddit debates . it uses a hierarchical generative Variational Autoencoder to model argumentation . |