Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021.findings-emnlp)
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| Challenge: | a method for user targeting is developed to identify online users to whom an ad should be targeted. |
| Approach: | They propose a method for automatic augmentation of positive and negative clickthrough data for user targeting models. |
| Outcome: | The proposed method can increase positive and negative instances of positive training instances on two datasets. |
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| Challenge: | Text-to-image models are appealing for customizing visual ads and targeting specific populations. |
| Approach: | We examine the disparate level of persuasiveness of ads that are identical except for gender/race of the people portrayed. |
| Outcome: | The proposed technique is based on a demographic bias analysis of ads for different topics and a disparate level of persuasiveness of ads that are identical except for gender/race of the people portrayed. |
Leveraging Similar Users for Personalized Language Modeling with Limited Data (2022.acl-long)
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| Challenge: | Recent work suggests that personalized models are more accurate for individual users than one-size-fits-all solutions. |
| Approach: | They propose a model trained on users that are similar to a new user to find similarity between new and existing users. |
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An Empirical Comparison of Instance Attribution Methods for NLP (2021.naacl-main)
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| Challenge: | Influence functions provide machinery for identifying training instances that may have led to a specific prediction, but are computationally expensive and prohibitive in many cases. |
| Approach: | They evaluate the degree to which different potential instance attribution agrees with respect to the importance of training samples. |
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Neural Network based Extreme Classification and Similarity Models for Product Matching (N18-3)
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| Challenge: | Matching a seller listed item to an appropriate product has become a fundamental step for e-commerce platforms. |
| Approach: | They propose to use a shallow neural network to match a seller's item to an appropriate product . they also propose a similarity approach based on deep siamese network to train and infer product information. |
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UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis (2022.naacl-main)
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Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
| Challenge: | Currently, global models are not able to produce personalized responses for individual users, based on their data. |
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Aspect-based Analysis of Advertising Appeals for Search Engine Advertising (2022.naacl-industry)
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| Challenge: | ad creators must consider various aspects of advertising appeals such as price, product features, and quality in their ac work. |
| Approach: | They propose to use a dataset of ad texts to explore the effective aspects of advertising appeals (A3) for different industries to assist a search engine ap creators. |
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Training Language Models under Resource Constraints for Adversarial Advertisement Detection (2021.naacl-industry)
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| Challenge: | e-commerce and social media sites require content moderation to ensure ethical standards . a tiered moderation workflow with automated components complements human experts . |
| Approach: | They propose techniques for training text classification models under resource constraints . they use weak supervision, curriculum learning and multi-lingual training to fine-tune BERT . |
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Denoising Attention for Query-aware User Modeling (2024.findings-naacl)
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| Challenge: | Recent work has proposed to build user models at query time by leveraging the Attention mechanism, which allows weighing the contribution of the user-related information w.r.t. the current query. |
| Approach: | They propose to use the Attention mechanism to build user models at query time by weighing the contribution of the user-related information w.r.t. the Attention variant adopts a robust normalization scheme and introduces . filtering mechanism to better discern among the user related data those helpful for personalization. |
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Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP (D19-59)
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| Challenge: | The first workshop on crowdsourcing for NLP is open to all . |
| Approach: | The first workshop on crowdsourcing annotations for NLP is held at the acl.com . the workshop will focus on methods for aggregating and analysing crowdsourced data for Nl-specific tasks. |
| Outcome: | The first workshop on crowdsourcing for NLP received 16 submissions and accepted 7 . the workshop will focus on ambiguous, subjective or ambiguity analysis of crowdsourced data . |
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named Entity Recognition (NER) systems perform well on in-distribution data, but perform poorly on examples drawn from a shifted distribution. |
| Approach: | They propose to use expert-guided heuristics to change entity tokens and their contexts to alter their entity types as adversarial attacks. |
| Outcome: | The proposed model significantly improves performance on the challenging set and out-of-domain generalization. |