| Challenge: | Prior work on controllable text generation assumes that the generated attribute can take on a finite set of values known a priori. |
| Approach: | They propose a task where the syntax of a generated sentence is controlled rather by a sentential exemplar. |
| Outcome: | The proposed model achieves improvements over baselines and learns to capture desirable characteristics. |
Similar Papers
Syntax-Guided Controlled Generation of Paraphrases (2020.tacl-1)
Copied to clipboard
| Challenge: | Recent work has explored the incorporation of complex syntactic-guidance as constraints in the task of controlled text generation. |
| Approach: | They propose an end-to-end framework for controlled paraphrase generation that incorporates complex syntactic-guidance constraints into the task. |
| Outcome: | The proposed framework generates syntax-conforming sentences while not compromising on relevance. |
Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
Copied to clipboard
| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
GCPG: A General Framework for Controllable Paraphrase Generation (2022.findings-acl)
Copied to clipboard
Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, Boxing Chen
| Challenge: | Existing studies highlight a special condition under two indispensable aspects of controllable paraphrase generation (CPG) individually, lacking a unified circumstance to explore and analyze their effectiveness. |
| Approach: | They propose a general controllable paraphrase generation framework that integrates lexical and syntactical conditions into a text sequence and uniformly processes them in an encoder-decoder paradigm. |
| Outcome: | The proposed framework can combine lexical and syntactical conditions and improve paraphrase generation. |
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021.eacl-main)
Copied to clipboard
| Challenge: | Paraphrase generation requires many annotated paraphrase pairs, which are expensive to obtain. |
| Approach: | They propose a model that learns to disentangle the semantics and syntax of a sentence from unannotated texts. |
| Outcome: | The proposed model learns to disentangle the semantics and syntax of a sentence from a collection of unannotated texts. |
Neural Syntactic Preordering for Controlled Paraphrase Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to paraphrasing natural language sentences are limited by the complexity of the task. |
| Approach: | They propose a framework for paraphrasing natural language sentences that uses syntactic transformations to softly "reorder" the source sentence and their proposed system is evaluated automatically and by humans . |
| Outcome: | The proposed model retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases. |
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks (N18-1)
Copied to clipboard
| Challenge: | Existing approaches to learn to do syntactically controlled paraphrase generation are limited . lexical, pragmatic, and syntaktic variation can hurt generalization of models trained on them . |
| Approach: | They propose a new approach for learning to do syntactically controlled paraphrase generation using a parser. |
| Outcome: | The proposed model generates paraphrases that follow their target specifications without decreasing paraphrase quality compared to baseline models . it improves the robustness of the models to syntactic variation when used to augment training data. |
Generating Syntactic Paraphrases (D18-1)
Copied to clipboard
| Challenge: | Using data-to-text generation, text-totext generation and text reduction, we show that conditioning text generation on syntactic constraints permits the generation of syntakically distinct paraphrases for the same input. |
| Approach: | They propose to use four different models for automatic generation of syntactic paraphrases to study the automatic generation process. |
| Outcome: | The proposed models can generate syntactic paraphrases for the same input and exploit different types of input to increase the number of distinct paraphrased for a given input. |
Exploring Controllable Text Generation Techniques (2020.coling-main)
Copied to clipboard
| Challenge: | Neural controllable text generation has a plethora of applications but there is no unifying theme. |
| Approach: | They propose a new schema for the control of attributes in the generation process by classifying it into five modules and providing an analysis on the advantages and disadvantages of these techniques. |
| Outcome: | The proposed frameworks can be used to control the attributes of natural sentences and to modulate the formality and politeness of emails. |
Don’t Change Me! User-Controllable Selective Paraphrase Generation (2021.eacl-main)
Copied to clipboard
| Challenge: | a new technique allows paraphrase generation to be user-controlled . a user looking for cheap hotels in New York would not find the other answer helpful . |
| Approach: | They propose a method that provides a user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" they propose allowing user-controllable paraphrase generation by fine-tuning model that exhibits this behavior . |
| Outcome: | The proposed technique is language agnostic and tested in English and Chinese. |
AESOP: Paraphrase Generation with Adaptive Syntactic Control (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for paraphrase generation use fixed syntactic structures for all input sentences. |
| Approach: | They propose to add syntactical control to a pretrained language model to generate fluent paraphrases using a retrieval-based selection module. |
| Outcome: | The proposed model achieves state-of-the-art on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntaktic control from human-annotated exemplars. |