Papers with fine-grained controllable generation
CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation (2022.emnlp-main)
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| Challenge: | Generating code-switched text with fine-grained control on the degree of code-witching and the lexical choices used to convey formality has been well-explored. |
| Approach: | They propose to generate code-switched text with fine-grained control on the degree of code-changing and lexical choices used to convey formality. |
| Outcome: | The proposed model can be invoked at test-time to synthesize code-switched text faithful to syntactic and lexical attributes relevant to code-witching. |
NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints (2021.naacl-main)
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| Challenge: | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. |
| Approach: | They propose an algorithm that enables neural language models to generate fluent text while satisfying complex lexical constraints. |
| Outcome: | The proposed algorithm outperforms existing methods on four benchmarks and shows that it handles any set of lexical constraints expressible under predicate logic while its asymptotic runtime is equivalent to conventional beam search. |
NEUROSTRUCTURAL DECODING: Neural Text Generation with Structural Constraints (2023.acl-long)
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| Challenge: | Current approaches for conditional text generation focus on lexical constraints, but lack syntactic constraints to support complex semantic constraints. |
| Approach: | They propose a decoding algorithm that incorporates syntactic constraints to improve the quality of the generated text. |
| Outcome: | The proposed method improves on three different language generation tasks and shows improved lexical and syntactic metrics. |