Topic-Guided Self-Introduction Generation for Social Media Users (2023.findings-acl)
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| Challenge: | Existing studies on social media use tags to profile users, but we have found that sentence-level self-introductions are more natural and engaging. |
| Approach: | They propose a novel topic-guided encoder-decoder framework that uses a user's tweeting history to generate a short sentence outlining their personal interests. |
| Outcome: | The proposed framework outperforms existing encoder-decoder models on a large-scale Twitter dataset and shows that it is more natural and engaging than previous approaches. |
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| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
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Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
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Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
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| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
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Engage the Public: Poll Question Generation for Social Media Posts (2021.acl-long)
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| Challenge: | a novel application to generate poll questions for social media posts offers an easy way to hear the public's voice . for the silent majority, they tend to read others' messages instead of voicing their opinions with words . |
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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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Latent Inter-User Difference Modeling for LLM Personalization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs. |
| Approach: | They propose a framework that models inter-user differences in the latent space instead of relying on language-based prompts. |
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Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations (P18-2)
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| Challenge: | Existing systems that use user and item identity as inputs for review generation are lacking in the field of natural language processing. |
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RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation (2023.acl-long)
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| Challenge: | Existing approaches to personalized dialogue generation rely on dialogue data paired with user traits, profiles or persona description sentences. |
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MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation (C18-1)
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| Challenge: | Neural encoder-decoder models tend to generate meaningless and generic responses regardless of what the input text is. |
| Approach: | They propose an easy-to-extend learning framework based on latent vectors to provide training guidance without resorting to extra data or complicating network’s inner structure. |
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Guided Profile Generation Improves Personalization with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to personalization with LLMs rely on sparse and complex personal contexts, resulting in incomplete interpretation. |
| Approach: | They propose a general method to generate personal profiles in natural language that extracts important, distinctive features from the personal context into concise, descriptive sentences. |
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