| Challenge: | Open-ended text generation tasks require models to generate coherent continuation given limited preceding context. |
| Approach: | They propose a novel two-stage method which explicitly arranges ensuing events in open-ended text generation tasks. |
| Outcome: | The proposed method improves coherence and diversity of open-ended text generation tasks. |
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
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Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)
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| Challenge: | Existing tools for text-to-image synthesis can visualize machine imaginations for a given context. |
| Approach: | They propose a framework that uses machine-generated images to guide language models in open-ended text generation. |
| Outcome: | The proposed framework is effective on open-ended text generation tasks while showing minor degeneration. |
Data-to-text Generation with Macro Planning (2021.tacl-1)
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| Challenge: | Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently . |
| Approach: | They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets . |
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Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence (2021.acl-long)
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| Challenge: | Existing generation models struggle to maintain a coherent event sequence throughout the generated text. |
| Approach: | They propose a long text generation model which can represent prefix sentences at sentence level and discourse level in the decoding process. |
| Outcome: | The proposed model can generate more coherent texts than state-of-the-art models. |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
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Look-back Decoding for Open-Ended Text Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to decode open-ended text have addressed degeneration problems in large-scale language models (LLMs) |
| Approach: | They propose an improved decoding algorithm that leverages the Kullback–Leibler divergence to track the distribution distance between current and historical decoding steps. |
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Returning to the Start: Generating Narratives with Related Endpoints (2024.naacl-short)
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| Challenge: | RENarGen generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Approach: | They propose a novel novel novel that generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
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Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation (N19-1)
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| Challenge: | Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. |
| Approach: | They propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. |
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Plan-then-Generate: Controlled Data-to-Text Generation via Planning (2021.findings-emnlp)
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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. |
| Approach: | They propose a Plan-then-Generate framework to improve the controllability of neural data-to-text models. |
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Towards Informative Open-ended Text Generation with Dynamic Knowledge Triples (2023.findings-emnlp)
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| Challenge: | Pretrained language models (PLMs) have impressive capabilities in open-ended text generation. |
| Approach: | They propose a dynamic knowledge-guided informative open-ended text generation approach that utilizes a knowledge graph to help the model generate more contextually related entities and detailed facts. |
| Outcome: | The proposed approach generates more informative texts than baselines. |