Papers with argumentation mining
Multi-Task Learning for Argumentation Mining in Low-Resource Settings (N18-2)
Copied to clipboard
| Challenge: | Argument component identification is difficult for trained annotators to perform in a new domain or to develop new AM tasks. |
| Approach: | They investigate whether multi-task learning can improve performance on AM problems . they found that MTL performs particularly well when little training data is available for the main task . |
| Outcome: | The proposed approach performs better when little training data is available for the main task, a common scenario in AM. |
Augmenting pre-trained language models with audio feature embedding for argumentation mining in political debates (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies on the integration of multimodality with text and audio in natural language processing tasks have focused on the use of image and text for emotion recognition, fake news detection and document image classification. |
| Approach: | They propose to integrate audio features with text in a task of argumentation mining using a previously reported dataset and an audio-enhanced version. |
| Outcome: | The proposed model outperforms text-based models on a dataset of 28,850 utterances on 'argumentation mining' with limited data. |
Computational Ad Hominem Detection (P19-2)
Copied to clipboard
| Challenge: | ad hominem attacks are introduced in debates as an easy win, but their impact on argumentation is limited . a machine learning approach to detect the personal attack is insufficient, we show . |
| Approach: | They propose a machine learning approach that detects ad hominem attacks using social media data . they propose TF-IDF approaches that are insufficient to detect the personal attack . |
| Outcome: | The proposed method has a recall of 80% for a social media data source. |
A Recorded Debating Dataset (L18-1)
Copied to clipboard
Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
| Challenge: | Existing research in computational argumentation and debating technologies focuses on argumentation mining, but other tasks are being addressed as well. |
| Approach: | They describe a dataset of debating speeches in English that is used for research . they use an automatic speech recognition system to produce a more "nLP-friendly" text . |
| Outcome: | The proposed dataset contains 60 speeches on various controversial topics, each in five formats corresponding to different stages in production. |
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. |
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion (P19-1)
Copied to clipboard
Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argumentation mining have left much of the relevant argumentative content out of reach. |
| Approach: | They propose a task of Debate Topic Expansion to find related topics for a given debate topic, along with an annotated dataset for the task. |
| Outcome: | The proposed algorithms differ from well-studied lexical-semantic relations and show they work well in argumentation mining. |
A Survey on Stance Detection for Mis- and Disinformation Identification (2022.findings-naacl)
Copied to clipboard
| Challenge: | Understanding attitudes expressed in texts plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentional false information). |
| Approach: | They examine the relationship between stance detection and mis- and disinformation detection online and examine the results of previous studies. |
| Outcome: | The proposed task is a component of fact-checking, rumour detection, and detecting previously fact- checked claims, and is compared with other related tasks such as argumentation mining and sentiment analysis. |
Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
Copied to clipboard
| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
| Approach: | They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks. |
| Outcome: | The proposed framework can be used for argumentation mining tasks without expensive inference tools. |
A Neural Transition-based Model for Argumentation Mining (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for identifying argumentation structures are inefficient and class imbalanced. |
| Approach: | They propose a neural transition-based model that incrementally builds an argumentation graph by generating a sequence of actions. |
| Outcome: | The proposed model can handle tree and non-tree structured argumentation without structural constraints. |
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. |