Share this page:

Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations

Kuan-Hao Huang, Varun Iyer, Anoop Kumar, Sriram Venkatapathy, Kai-Wei Chang, and Aram Galstyan, in EMNLP-Finding (short), 2022.

Download the full text

Abstract

Syntactically controlled paraphrase generation has become an emerging research direction in recent years. Most existing approaches require annotated paraphrase pairs for training and are thus costly to extend to new domains. Unsupervised approaches, on the other hand, do not need paraphrase pairs but suffer from relatively poor performance in terms of syntactic control and quality of generated paraphrases. In this paper, we demonstrate that leveraging Abstract Meaning Representations (AMR) can greatly improve the performance of unsupervised syntactically controlled paraphrase generation. Our proposed model, AMR-enhanced Paraphrase Generator (AMRPG), separately encodes the AMR graph and the constituency parse of the input sentence into two disentangled semantic and syntactic embeddings. A decoder is then learned to reconstruct the input sentence from the semantic and syntactic embeddings. Our experiments show that AMRPG generates more accurate syntactically controlled paraphrases, both quantitatively and qualitatively, compared to the existing unsupervised approaches. We also demonstrate that the paraphrases generated by AMRPG can be used for data augmentation to improve the robustness of NLP models.

Bib Entry

@inproceedings{huang2022unsupervised,
  title = {Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations},
  author = {Huang, Kuan-Hao and Iyer, Varun and Kumar, Anoop and Venkatapathy, Sriram and Chang, Kai-Wei and Galstyan, Aram},
  booktitle = {EMNLP-Finding (short)},
  year = {2022}
}

Related Publications

  1. VideoCon: Robust video-language alignment via contrast captions, CVPR, 2024
  2. CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning, ICCV, 2023
  3. Red Teaming Language Model Detectors with Language Models, TACL, 2023
  4. ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation, EMNLP, 2022
  5. Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers, EMNLP-Finding (short), 2022
  6. Improving the Adversarial Robustness of NLP Models by Information Bottleneck, ACL-Finding, 2022
  7. Searching for an Effiective Defender: Benchmarking Defense against Adversarial Word Substitution, EMNLP, 2021
  8. On the Transferability of Adversarial Attacks against Neural Text Classifier, EMNLP, 2021
  9. Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble, ACL, 2021
  10. Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation, NAACL, 2021
  11. Provable, Scalable and Automatic Perturbation Analysis on General Computational Graphs, NeurIPS, 2020
  12. On the Robustness of Language Encoders against Grammatical Errors, ACL, 2020
  13. Robustness Verification for Transformers, ICLR, 2020
  14. Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification, EMNLP, 2019
  15. Retrofitting Contextualized Word Embeddings with Paraphrases, EMNLP (short), 2019
  16. Generating Natural Language Adversarial Examples, EMNLP (short), 2018