Zichao Li

Tied-Augment: Controlling Representation Similarity Improves Data Augmentation

2023 · arXiv (Cornell University)

Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi-supervised, self-supervised, and supervised training for vision. Despite incurring …

EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing

2019

We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine …

Decomposable Neural Paraphrase Generation

2019

Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence …

Paraphrase Generation with Deep Reinforcement Learning

2018

Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose …