Papers by Khalid Al Khatib

14 papers
Employing Argumentation Knowledge Graphs for Neural Argument Generation (2021.acl-long)

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Challenge: Existing methods for generating arguments use end-to-end knowledge graphs or are controlled with respect to the argument's topic, aspects, or stance.
Approach: They construct and populate three knowledge graphs and encode them into debate portals and relevant paragraphs from Wikipedia.
Outcome: The proposed model produces arguments with superior quality than those generated without knowledge.
Detecting Media Bias in News Articles using Gaussian Bias Distributions (2020.findings-emnlp)

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Challenge: a new study shows that media bias is not only about honesty or accuracy, but also about taste or preference.
Approach: They propose to use second-order information to detect media bias in articles . they propose to analyze the frequency, positions, and sequential order of biased statements .
Outcome: The proposed model outperforms other models that use second-order information on biased statements on an existing media bias dataset.
Generating Informative Conclusions for Argumentative Texts (2021.findings-acl)

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Challenge: Argumentative texts often omit explicit conclusions, expecting readers to infer them rather . a corpus of 136,996 arguments is compiled and used to generate informative conclusions .
Approach: They propose to generate informative conclusions from a large-scale corpus of argumentative texts . they propose to use argumentative knowledge to augment the corpus and refine the model .
Outcome: The proposed corpus of argumentative texts and their conclusions is compiled and analyzed . the results show that the proposed model is informative and concise .
Controlled Neural Sentence-Level Reframing of News Articles (2021.findings-emnlp)

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Challenge: a news article is framed from a specific perspective, but reframing can be difficult . a framed article can be used to communicate with opposing camps of audiences .
Approach: They propose to reframe news articles using a media frame corpus to achieve this . they propose three strategies to train neural models for reframing .
Outcome: The proposed techniques maintain coherence of sentences and reframe them correctly . the proposed techniques are effective but have tradeoffs .
Analyzing the Persuasive Effect of Style in News Editorial Argumentation (2020.acl-main)

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Challenge: Existing research has investigated the persuasive effect of content and style on argumentative content.
Approach: They compare the style of news editorials with ideology-specific effect annotations to find out how important it is to achieve persuasiveness.
Outcome: The proposed method shows that conservative readers are resistant to style on liberal editorials, whereas conservative readers resist style on conservatives.
Summary Explorer: Visualizing the State of the Art in Text Summarization (2021.emnlp-demo)

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Challenge: Automatic text summarization is the task of generating a summary of a long text by condensing it to its most important parts.
Approach: They propose a tool to visually explore document summarization systems based on three well-known summary quality criteria .
Outcome: The proposed tool compiles outputs of 55 state-of-the-art document summarization approaches and visually explores them during a qualitative assessment.
Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation (2022.findings-aacl)

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Challenge: a theoretical model of bias classification is not feasible because of complexity of interpreting language phenomena.
Approach: They propose to analyze whether a text is biased based on an algorithmic analysis . they propose to use a model to determine whether x is more biased than y .
Outcome: a crowdsourcing study shows that differences in stance bias are perceptible when (light) support is provided through training or visual aids.
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (D19-50)

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Challenge: Existing methods to detect abusive language only train one classifier for the whole variety of offending . a new method can support a moderator with explicit unraveled explanations for why something was flagged as abusive .
Approach: a new method is proposed to distinguish explicitly abusive cases from the more "shadowed" ones . the researchers extend a lexicon of abusive terms to include new obfuscations of abusive words .
Outcome: a new method can distinguish explicitly abusive cases from the more "shadowed" ones . the method can support a moderator with explicit unraveled explanations for why something was flagged as abusive .
Reference-guided Style-Consistent Content Transfer (2024.lrec-main)

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Challenge: Text style transfer involves changing the style of a text while preserving its original style.
Approach: They propose a task of style-consistent content transfer which involves modifying a text’s content based on a provided reference statement while preserving its original style.
Outcome: The proposed approach meets three important conditions: reference faithfulness, style adherence, and coherence.
Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness (2020.acl-main)

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Challenge: Several studies have examined persuasiveness in debates by probing the main factors for establishing persuasion, particularly regarding the role of linguistic features of debaters' arguments.
Approach: They propose to model debaters’ prior beliefs, interests, and personality traits based on their previous activity without dependence on explicit user profiles or questionnaires.
Outcome: The proposed model improves persuasiveness prediction and debater resistance to persuasion.
Crawling and Preprocessing Mailing Lists At Scale for Dialog Analysis (2020.acl-main)

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Challenge: a new neural segmentation model is used to segment 153 million emails . email is perhaps the most reliable and ubiquitous means of digital communication .
Approach: They present a new neural segmentation model that crawls 153 million emails . it achieves 96% accuracy on 15 classes of email segments .
Outcome: The proposed model achieves state-of-the-art performance while being more efficient to train than previous ones.
News Editorials: Towards Summarizing Long Argumentative Texts (2020.coling-main)

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Challenge: Using news summarization, we aim to target opinionated articles with a well-defined argumentation structure.
Approach: They present a corpus of carefully curated summaries for 266 news editorials.
Outcome: The summarization of opinionated articles with a well-defined argumentation structure is evaluated using a tailored annotation scheme.
TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization (2024.eacl-demo)

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Challenge: TL;DR Progress is a literature explorer designed specifically for the text summarization literature.
Approach: They propose to organize 514 papers based on a comprehensive annotation scheme for text summarization approaches and a fine-grained, faceted search.
Outcome: The proposed tool organizes 514papers based on a comprehensive annotation scheme for text summarization approaches and enables fine-grained, faceted search.
Analyzing Persuasion Strategies of Debaters on Social Media (2022.coling-1)

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Challenge: Existing studies on the analysis of persuasion in online discussions focus on the effectiveness of comments in individual discussions and ignore the effectiveness analysis of debaters over multiple discussions.
Approach: They propose to quantify debaters effectiveness in the online discussion platform "ChangeMyView" they aim to explore diverse insights into their persuasion strategies .
Outcome: The proposed analysis of debater effectiveness in the ChangeMyView subreddit reveals that debaters have different levels of effectiveness, behavioral characteristics and text stylistic features .

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