Machine-learning based dialogue managers are able to learn complex behaviors
in order to complete a task, but it is not straightforward to extend their
capabilities to new domains. We investigate different policies' ability to
handle uncooperative user behavior, and how well expertise in completing one
task (such as restaurant reservations) can be reapplied when learning a new one
(e.g. booking a hotel). We introduce the Recurrent Embedding Dialogue Policy
(REDP), which embeds system actions and dialogue states in the same vector
space. REDP contains a memory component and attention mechanism based on a
modified Neural Turing Machine, and significantly outperforms a baseline LSTM
classifier on this task. We also show that both our architecture and baseline
solve the bAbI dialogue task, achieving 100% test accuracy.

我们提出使用 Recurrent Embedding Dialogue Policy（REDP）和 Neural Turing Machine 技术，对机器人会话管理进行研究。研究结果表明 REDP 能够有效地处理顾客的不良行为，并在 bAbI 对话任务中实现了 100％的测试准确率。

跨对话任务的少样本通用化

Few-Shot Generalization Across Dialogue Tasks

We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence
learning, which performs the task through a series of nonlinear transformations
from the representation of the input sequence (e.g., a Chinese sentence) to the
final output sequence (e.g., translation to English). Inspired by the recently
proposed Neural Turing Machine (Graves et al., 2014), we store the intermediate
representations in stacked layers of memories, and use read-write operations on
the memories to realize the nonlinear transformations between the
representations. The types of transformations are designed in advance but the
parameters are learned from data. Through layer-by-layer transformations,
DEEPMEMORY can model complicated relations between sequences necessary for
applications such as machine translation between distant languages. The
architecture can be trained with normal back-propagation on sequenceto-sequence
data, and the learning can be easily scaled up to a large corpus. DEEPMEMORY is
broad enough to subsume the state-of-the-art neural translation model in
(Bahdanau et al., 2015) as its special case, while significantly improving upon
the model with its deeper architecture. Remarkably, DEEPMEMORY, being purely
neural network-based, can achieve performance comparable to the traditional
phrase-based machine translation system Moses with a small vocabulary and a
modest parameter size.

DEEPMEMORY 是一种基于神经网络的序列到序列学习框架，采用非线性转换方法来将输入序列（比如中文句子）转换到目标输出序列（比如翻译成英文），其使用内存的堆叠层存储中间表示并进行读写操作来实现从一种表示到另一种表示的非线性转换，这种方法可以处理机器翻译等领域的非常复杂的序列数据，同时 DEEPMEMORY 可以通过正常的反向传播算法进行训练。

序列到序列学习的深度基于记忆的架构

A Deep Memory-based Architecture for Sequence-to-Sequence Learning

The Neural Turing Machine (NTM) is more expressive than all previously
considered models because of its external memory. It can be viewed as a broader
effort to use abstract external Interfaces and to learn a parametric model that
interacts with them.
The capabilities of a model can be extended by providing it with proper
Interfaces that interact with the world. These external Interfaces include
memory, a database, a search engine, or a piece of software such as a theorem
verifier. Some of these Interfaces are provided by the developers of the model.
However, many important existing Interfaces, such as databases and search
engines, are discrete.
We examine feasibility of learning models to interact with discrete
Interfaces. We investigate the following discrete Interfaces: a memory Tape, an
input Tape, and an output Tape. We use a Reinforcement Learning algorithm to
train a neural network that interacts with such Interfaces to solve simple
algorithmic tasks. Our Interfaces are expressive enough to make our model
Turing complete.

本研究通过使用带有外部存储器的神经图灵机，并提供相应的接口来扩展模型的能力，研究了使用离散接口（包括记忆带、输入带和输出带），通过强化学习方法训练神经网络来解决简单算法任务的可行性。我们的接口足够表达模型的图灵完备性。