39 citations · 89 across the 6 of their papers we have counts for
7 papers · 1 filter
Bird-Eye Transformers for Text Generation Models
Lei Sha, Yuhang Song, Yordan Yordanov +2
Transformers have become an indispensable module for text generation models since their great success in machine translation. Previous works attribute the~success of transformers t…
Controlling Text Edition by Changing Answers of Specific Questions
Lei Sha, Patrick Hohenecker, Thomas Lukasiewicz
In this paper, we introduce the new task of controllable text edition, in which we take as input a long text, a question, and a target answer, and the output is a minimally modifie…
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration
Lei Sha, Oana-Maria Camburu, Thomas Lukasiewicz
Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive…
Multi-type Disentanglement without Adversarial Training
Lei Sha, Thomas Lukasiewicz
Controlling the style of natural language by disentangling the latent space is an important step towards interpretable machine learning. After the latent space is disentangled, the…
Table-to-text Generation by Structure-aware Seq2seq Learning
Tianyu Liu, Kexiang Wang, Lei Sha +2
Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a t…
Order-Planning Neural Text Generation From Structured Data
Lei Sha, Lili Mou, Tianyu Liu +4
Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, r…