Controlling Text Edition by Changing Answers of Specific Questions
By Lei Sha, Patrick Hohenecker, Thomas Lukasiewicz. 2021
7 citations recorded by the source corpus.
In this paper, we introduce the new task of controllable text edition, in which we take as input a long text, a question, and a target answer, and the output is a minimally modified text, so that it fits the target answer. This task is very important in many situations, such as changing some conditions, consequences, or properties in a legal document, or changing some key information of an event in a news text. This is very challenging, as it is hard to obtain a parallel corpus for training, and we need to first find all text positions that should be changed and then decide how to change them. We constructed the new dataset WikiBioCTE for this task based on the existing dataset WikiBio (originally created for table-to-text generation). We use WikiBioCTE for training, and manually labeled a test set for testing. We also propose novel evaluation metrics and a novel method for solving the new task. Experimental results on the test set show that our proposed method is a good fit for this novel NLP task.
Subfields: Natural Language Processing
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In Natural Language Processing
- Matt Gardner
- Matthew E. Peters
- Ana Marasovic
- Tongshuang Sherry Wu
- Hao Peng
- Alexis Ross
- Mohit Bansal
- Hannaneh Hajishirzi
Citations in the corpus
- Tailor: Generating and Perturbing Text with Semantic Controls (2021, background): “They usually rely on application-specific labels (Ross et al., 2021; Madaan et al., 2020b; Sha et al., 2021; Akyürek et al., 2021) or require pairs of original and perturbed sentences (Wu et al., 2021), which are expensive to generalize.”
- Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2020, methodology): “Very recent work by Sha et al. (2021), introduced after the submission of M I CE, proposes a method for targeted contrastive editing for Q&A that selects answer-related tokens, masks them, and generates new tokens.”