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Papers with advertising

OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising (2025.coling-industry)

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Zhao Wang, Briti Gangopadhyay, Mengjie Zhao, Shingo Takamatsu

Challenge:	Conventionally, keyword decision-making in sponsored search advertising relies on deep generation-based methods.
Approach:	They propose an LLM agent-based method that dynamically monitors KPI changes and adapts keyword generation in real-time.
Outcome:	The proposed method shows significant improvements across various metrics and emphasizes the importance of each component.

CULG: Commercial Universal Language Generation (2022.naacl-industry)

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Haonan Li, Yameng Huang, Yeyun Gong, Jian Jiao, Ruofei Zhang, Timothy Baldwin, Nan Duan

Language Models

Challenge:	Pre-trained language models have improved performance for many NLP tasks in finance and healthcare.
Approach:	They propose a large-scale commercial universal language generation model which is pre-trained on a corpus drawn from 10 markets across 7 languages.
Outcome:	The proposed model outperforms other models on commercial generation tasks and on other markets, languages, and tasks.

SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)

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Cao Te, Mengge Xue, Zhenyu Hu, Yuan Chen, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Multimodality

Challenge:	Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity.
Approach:	They propose a framework for the minimalist rectification of non-compliant image ads.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency.

NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey (2026.findings-eacl)

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Dhiman Goswami, Jai Kruthunz Naveen Kumar, Sanchari Das

Responsible & Trustworthy NLP

Challenge:	Social media platforms such as X (formerly Twitter), Facebook, and Reddit generate user-generated content.
Approach:	They propose a framework to assess privacy risks in social media by evaluating vulnerabilities across six dimensions: data collection, preprocessing, visibility, fairness, computational risk, and regulatory compliance.
Outcome:	The proposed framework assesses privacy risks across six dimensions . it achieves F1-scores of 0.58–0.84, but incurs 1% - 23% drop under fine-tuning .

Examining the Ordering of Rhetorical Strategies in Persuasive Requests (2020.findings-emnlp)

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Omar Shaikh, Jiaao Chen, Jon Saad-Falcon, Polo Chau, Diyi Yang

Challenge:	Numerous studies have been conducted to understand persuasiveness of text, from explorations of rhetoric in presidential campaigns to the impact of a communicator's likability on persuasiveness.
Approach:	They use a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus and visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual request.
Outcome:	The proposed model disentangles content and rhetorical strategies in textual requests from a large-scale loan request corpus and visualizes interplay between content and strategy through attentional LSTM that predicts the success rate of textual request.

Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements (2020.coling-main)

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Andrey Savchenko, Anton Alekseev, Sejeong Kwon, Elena Tutubalina, Evgeny Myasnikov, Sergey Nikolenko

Information Retrieval

Challenge:	a recent study shows that image-based symbols are insufficient for symbolism prediction in visual advertising . a new method is proposed to help understand image advertisements .
Approach:	They propose a multimodal image-based classifier and object detection classifier for symbols . they propose 'symbolic' annotation tasks to help users understand ads'
Outcome:	The proposed system establishes state-of-the-art in symbolism prediction.

Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (N19-1)

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Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, Eduard Hovy

Responsible & Trustworthy NLP

Challenge:	Existing models can't quantify persuasiveness of requests or extract successful persuasive strategies.
Approach:	They propose a semi-supervised hierarchical neural network model to quantify persuasiveness and identify persuasive strategies in advocacy requests.
Outcome:	The proposed method outperforms baseline models and offers increased interpretability of persuasive speech.

Training Data Augmentation for Code-Mixed Translation (2021.naacl-main)

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Abhirut Gupta, Aditya Vavre, Sunita Sarawagi

Multilinguality

Challenge:	We show a 5.8 point increase in BLEU on heavily code-mixed sentences . code-mixing is becoming more commonplace in several bilingual communities .
Approach:	They propose a method to convert existing parallel data sources into code-mixed parallel data.
Outcome:	The proposed method shows a 5.8 point increase in BLEU on heavily code-mixed sentences on a Hindi-English code-mixed translation task.

Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness (2020.acl-main)

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Khalid Al Khatib, Michael Völske, Shahbaz Syed, Nikolay Kolyada, Benno Stein

Reasoning

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.

USDC: A Dataset of ̲User ̲Stance and ̲Dogmatism in Long ̲Conversations (2025.findings-acl)

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Mounika Marreddy, Subba Reddy Oota, Venkata Charan Chinni, Manish Gupta, Lucie Flek

Text Classification

Challenge:	Previously, studies on stance and dogmatism in user conversations have focused on training models using annotated datasets at the post level, treating each post as independent and randomly sampling posts from conversation threads.
Approach:	They build a dataset for studying user opinion fluctuations in 764 long multi-user Reddit conversation threads, called USDC.
Outcome:	The proposed dataset analyzes user opinion fluctuations in 764 long multi-user Reddit conversation threads.

SkOTaPA: A Dataset for Skepticism Detection in Online Text after Persuasion Attempt (2024.lrec-main)

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Smitha Muthya Sudheendra, Maral Abdollahi, Dongyeop Kang, Jisu Huh, Jaideep Srivastava

Sentiment Analysis

Challenge:	Persuasion attempts are a form of persuaded behavior that can be observed in various social settings, such as advertising, public health, political campaigns, and personal relationships.
Approach:	They propose to use multiple independent human annotations to detect skepticism in response to persuasion attempts on social media influencer marketing.
Outcome:	The proposed corpus detects skepticism in response to persuasion attempts on social media influencer marketing using multiple independent human annotations.