Chinese pinyin input method engine (IME) converts pinyin into character so
that Chinese characters can be conveniently inputted into computer through
common keyboard. IMEs work relying on its core component, pinyin-to-character
conversion (P2C). Usually Chinese IMEs simply predict a list of character
sequences for user choice only according to user pinyin input at each turn.
However, Chinese inputting is a multi-turn online procedure, which can be
supposed to be exploited for further user experience promoting. This paper thus
for the first time introduces a sequence-to-sequence model with gated-attention
mechanism for the core task in IMEs. The proposed neural P2C model is learned
by encoding previous input utterance as extra context to enable our IME capable
of predicting character sequence with incomplete pinyin input. Our model is
evaluated in different benchmark datasets showing great user experience
improvement compared to traditional models, which demonstrates the first
engineering practice of building Chinese aided IME.

本研究介绍了一种基于序列到序列模型和门控注意机制的中文输入法引擎，通过编码先前输入内容，令输入法能够在仅输入部分拼音的情况下准确预测汉字，从而显著提升用户体验。

汉语拼音辅助输入法：输入未按键入的内容

Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet

To perform tasks specified by natural language instructions, autonomous
agents need to extract semantically meaningful representations of language and
map it to visual elements and actions in the environment. This problem is
called task-oriented language grounding. We propose an end-to-end trainable
neural architecture for task-oriented language grounding in 3D environments
which assumes no prior linguistic or perceptual knowledge and requires only raw
pixels from the environment and the natural language instruction as input. The
proposed model combines the image and text representations using a
Gated-Attention mechanism and learns a policy to execute the natural language
instruction using standard reinforcement and imitation learning methods. We
show the effectiveness of the proposed model on unseen instructions as well as
unseen maps, both quantitatively and qualitatively. We also introduce a novel
environment based on a 3D game engine to simulate the challenges of
task-oriented language grounding over a rich set of instructions and
environment states.

提出一种基于端到端可训练神经网络架构，用于在 3D 环境中执行自然语言指令的任务导向语言接地问题，并使用带门控的注意力机制来组合图像和文本表示，并学习执行任务的策略。在一个新的基于 3D 游戏引擎的环境中展示了该模型在未见过的指令和环境下的有效性。