Papers with neural generation
Controllable Neural Dialogue Summarization with Personal Named Entity Planning (2021.emnlp-main)
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| Challenge: | Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations. |
| Approach: | They propose a controllable neural generation framework that can guide dialogue summarization with personal named entity planning. |
| Outcome: | The proposed framework generates fluent and factually consistent summaries under various planning controls using objective metrics and human evaluations. |
GO FIGURE: A Meta Evaluation of Factuality in Summarization (2021.findings-acl)
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| Challenge: | Recent advances in text generation systems produce fluent, coherent, relevant, and factually correct text. |
| Approach: | They propose a metaevaluation framework for evaluating factuality evaluation metrics . they propose five necessary conditions to evaluate factual metrics on diagnostic factuity data . |
| Outcome: | The proposed framework provides robust evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. |
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech (2021.acl-long)
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| Challenge: | Existing studies on generating hate speech/counter narratives have failed to reach high-quality datasets. |
| Approach: | They propose a human-in-the-loop data collection methodology that refines a generative language model iteratively by using its own data from previous loops to generate new training samples. |
| Outcome: | The proposed method is the only expert-based multi-target HS/CN dataset available to the community. |
Towards Content Transfer through Grounded Text Generation (N19-1)
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| Challenge: | Recent work in neural natural language generation has attracted significant interest in controlling the form of text, such as style, persona, and wordiness. |
| Approach: | They propose a task where the task is to generate a next sentence in a document that fits its context and is grounded in . external textual source such as a news story. |
| Outcome: | The proposed task is based on 640k Wikipedia referenced sentences paired with the source articles to show significant improvements against baselines. |
LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues (2026.acl-long)
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Fanyu Wang, Xiaoxi Kang, Paul Burgess, Aashish Srivastava, Chetan Arora, Adnan Trakic, Lay-Ki Soon, Md Khalid Hossain, Lizhen Qu
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities, but their precision remains inadequate. |
| Approach: | They propose a framework that integrates neural generation with statistical reasoning to improve the accuracy of large language models. |
| Outcome: | The proposed framework achieves interpretability through transparent feature weighting while maintaining data efficiency through correlation-based statistical classification. |