Tao Ge

Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study

2019

Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on …

Parallel Data Augmentation for Formality Style Transfer

2020 · arXiv (Cornell University)

The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation …

K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning

2024 · arXiv (Cornell University)

Strategic reasoning is a complex yet essential capability for intelligent agents. It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. Unlike static reasoning tasks, success in these contexts depends …

Fluency Boost Learning and Inference for Neural Grammatical Error Correction

2018

Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction (GEC) have two limitations: (1) a seq2seq model may not be well generalized with only limited error-corrected data; (2) a seq2seq model may fail …

Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study

2018 · arXiv (Cornell University)

Neural sequence-to-sequence (seq2seq) approaches have proven to be successful in grammatical error correction (GEC). Based on the seq2seq framework, we propose a novel fluency boost learning and inference mechanism. Fluency boosting learning generates diverse error-corrected …

Formality Style Transfer with Hybrid Textual Annotations

2019 · arXiv (Cornell University)

Formality style transformation is the task of modifying the formality of a given sentence without changing its content. Its challenge is the lack of large-scale sentence-aligned parallel data. In this paper, we propose an omnivorous …