Papers with guided model
Guiding Zero-Shot Paraphrase Generation with Fine-Grained Control Tokens (2023.starsem-1)
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| Challenge: | Sequence-to-sequence paraphrase generation models struggle with the generation of diverse paraphrases. |
| Approach: | They propose a translation-based guided paraphrase generation model that learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
| Outcome: | The proposed model learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data. |
Guiding Visual Question Generation (2022.naacl-main)
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| Challenge: | Existing approaches to Visual Question Generation (VQG) are trained to mimic an arbitrary choice of concept but only one or a few are captured by the human references. |
| Approach: | They propose a variant of Visual Question Generation which conditions the question generator on categorical information based on expectations on the type of question and the objects it should explore. |
| Outcome: | The proposed model improves on the current state of the art on an answer-category augmented VQA dataset and human evaluation validates that guidance helps the generation of questions that are grammatically coherent and relevant to the given image and objects. |
GSum: A General Framework for Guided Neural Abstractive Summarization (2021.naacl-main)
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| Challenge: | Abstractive summarization models are flexible, but they can be difficult to control. |
| Approach: | They propose a general and extensible guided summarization framework that takes different kinds of guidance as input and perform experiments across different varieties. |
| Outcome: | The proposed framework can generate more faithful summaries and different types of guidance generate qualitatively different summary. |