Papers with sentence planning
Natural Language Generation by Hierarchical Decoding with Linguistic Patterns (N18-2)
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| Challenge: | a common and mostly adopted method is the rule-based (or template-based) method for natural language generation. |
| Approach: | They propose a hierarchical decoding NLG model based on linguistic patterns in different levels. |
| Outcome: | The proposed method outperforms the traditional one with a smaller model size. |
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue (P19-1)
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| Challenge: | Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. |
| Approach: | They propose using tree-structured semantic representations for better discourse-level structuring and sentence-level planning and introduce a challenging dataset using this representation for the weather domain. |
| Outcome: | The proposed model improves discourse-level structuring and sentence-level planning on a weather domain and can be decoded to improve semantic correctness. |
AggGen: Ordering and Aggregating while Generating (2021.acl-long)
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| Challenge: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |
| Approach: | AggGen re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. |
| Outcome: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |
Attribute First, then Generate: Locally-attributable Grounded Text Generation (2024.acl-long)
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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. |
| Approach: | They propose a locally-attributable text generation approach prioritizing concise attributions by identifying relevant source segments and conditioning the generation process on them. |
| Outcome: | The proposed method yields more concise citations than baselines and significantly reduces time required for fact verification by human assessors. |