GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification (2025.coling-main)
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| Challenge: | Existing models generate text on demand, but in real-life situations, individuals do not continuously generate text or voice opinions. |
| Approach: | They propose a novel task to identify news-triggered opinion expressing timing by using a dataset generated by professional stock analysts. |
| Outcome: | The proposed model can generate opinion on stock analysts' actions and improves performance in various opinion understanding tasks. |
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| Challenge: | Existing neural generation models fall short of coherence, thus requiring efficient content planning. |
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| Challenge: | Recent efforts to address hallucinations in Large Language Models have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. |
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| Challenge: | a large body of research has been done on aspect-based sentiment analysis (ABSA) for almost two decades . aspect-Based sentiment analysis is a task that extracts sentiment/opinions from text in terms of targets . |
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| Challenge: | Existing evaluation methods for natural language generation are inadequate . distinguishing machine-generated text is challenging even for human evaluators . |
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Transition-based Opinion Generation for Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Existing approaches to aspect-based sentiment analysis often overlook the importance of explicitly modeling structure among sentiment elements. |
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| Challenge: | Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users. |
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DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts (2021.acl-long)
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Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, Yejin Choi
| Challenge: | Decoding-time Experts is a decoding- time method for controlled text generation . it combines a pretrained language model with "expert" LMs and/or "anti-expert" experts . |
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Time-aware Prompting for Text Generation (2022.findings-emnlp)
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| Challenge: | a new study investigates the effects of incorporating timestamps into generation systems . textual prompts focus more on non-temporal information and are less sensitive to given timestams . |
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CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning (2022.naacl-industry)
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| Challenge: | a paper focuses on automatically generating the text of an ad to capture user interest for achieving higher click-through rate. |
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ATGen: A Framework for Active Text Generation (2025.acl-demo)
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Akim Tsvigun, Daniil Vasilev, Ivan Tsvigun, Ivan Lysenko, Talgat Bektleuov, Aleksandr Medvedev, Uliana Vinogradova, Nikita Severin, Mikhail Mozikov, Andrey Savchenko, Ilya Makarov, Grigorev Rostislav, Ramil Kuleev, Fedor Zhdanov, Artem Shelmanov
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