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Yao Meng

4 accepted papers

2022

Learning Structural Information for Syntax-Controlled Paraphrase Generation

NAACL 2022findings

Syntax-controlled paraphrase generation aims to produce paraphrase conform to given syntactic patterns. To address this task, recent works have started to use parse trees (or syntactic templates) to guide generation.A constituency parse tree contains abundant structural information, such as parent-c…

2021

Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel Data

EMNLP 2021main

Previous works on syntactically controlled paraphrase generation heavily rely on large-scale parallel paraphrase data that is not easily available for many languages and domains. In this paper, we take this research direction to the extreme and investigate whether it is possible to learn syntactical…

2020

A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning

COLING 2020main

Paraphrase generation (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications. Recently, neural sequence-to-sequence (Seq2Seq) models have shown promi…

Cited by 19SourcePDFScholar
2020

Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling

COLING 2020main

Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding. However, many joint models still suffer from the robustness problem, especially on noisy inputs or rare/unseen events. To address this issue, we propose a Joint A…

Cited by 10SourcePDFScholar