Active learning is an efficient approach for mitigating data dependency when training neural
machine translation (NMT) models. In this paper, we explore new training frameworks by …

Conventional knowledge distillation (KD) approaches are commonly employed to compress
neural machine translation (NMT) models. However, they only obtain one lightweight …

The final frontier for simulation is the accurate representation of complex, real-world social
systems. While agent-based modeling (ABM) seeks to study the behavior and interactions of …

Although teacher forcing has become the main training paradigm for neural machine
translation, it usually makes predictions only conditioned on past information, and hence …

Despite the dramatic success in image generation, Generative Adversarial Networks (GANs)
still face great challenges in text generation. The difficulty in generator training arises from …

This paper presents a new dual domain mapping based on a unified bidirectional sequence-
to-sequence (seq2seq) learning. Traditionally, dual learning in domain mapping was …

Abstract Domain adaptation for neural machine translation (NMT) has always been a hot
research topic in the community of machine translation. Generally, previous studies focus on …

Unsupervised text style transfer aims to alter the underlying style of the text to a desired
value while keeping its style-independent semantics, without the support of parallel training …

Mutual learning, where multiple agents learn collaboratively and teach one another, has
been shown to be an effective way to distill knowledge for image classification tasks. In this …

Complex news events, such as natural disasters and socio-political conflicts, require swift
responses from the government and society. Relying on historical events to project the future …