This paper presents our novel method to encode word confusion networks, which
can represent a rich hypothesis space of automatic speech recognition systems,
via recurrent neural networks. We demonstrate the utility of our approach for
the task of dialog state tracking in spoken dialog systems that relies on
automatic speech recognition output. Encoding confusion networks outperforms
encoding the best hypothesis of the automatic speech recognition in a neural
system for dialog state tracking on the well-known second Dialog State Tracking
Challenge dataset.

本文介绍了一种用循环神经网络对单词混淆网络进行编码的新方法，该方法可以代表自动语音识别系统的丰富假设空间，在口语系统的对话状态跟踪任务中，编码混淆网络优于编码自动语音识别的最佳假设。

应用循环神经网络编码词汇混淆网络以进行对话状态跟踪

Encoding Word Confusion Networks with Recurrent Neural Networks for  Dialog State Tracking

Machine reading using differentiable reasoning models has recently shown
remarkable progress. In this context, End-to-End trainable Memory Networks,
MemN2N, have demonstrated promising performance on simple natural language
based reasoning tasks such as factual reasoning and basic deduction. However,
other tasks, namely multi-fact question-answering, positional reasoning or
dialog related tasks, remain challenging particularly due to the necessity of
more complex interactions between the memory and controller modules composing
this family of models. In this paper, we introduce a novel end-to-end memory
access regulation mechanism inspired by the current progress on the connection
short-cutting principle in the field of computer vision. Concretely, we develop
a Gated End-to-End trainable Memory Network architecture, GMemN2N. From the
machine learning perspective, this new capability is learned in an end-to-end
fashion without the use of any additional supervision signal which is, as far
as our knowledge goes, the first of its kind. Our experiments show significant
improvements on the most challenging tasks in the 20 bAbI dataset, without the
use of any domain knowledge. Then, we show improvements on the dialog bAbI
tasks including the real human-bot conversion-based Dialog State Tracking
Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state
of the art.

本文提出了一种新的、受计算机视觉中连接快捷原则启发的端到端可训练内存网络 (End-to-End trainable Memory Networks) 结构 ——Gated End-to-End trainable Memory Networks (GMemN2N)，该结构无需使用任何额外的监督信号，可以显著提高 20 bAbI 数据集的最具挑战性的任务和真人机器人对话 (DSTC-2) 数据集。

门控端到端记忆网络

Gated End-to-End Memory Networks

The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the
previous three editions as follows: the number of slot-value pairs present in
the ontology is much larger, no spoken language understanding output is given,
and utterances are labeled at the subdialog level. This paper describes a novel
dialog state tracking method designed to work robustly under these conditions,
using elaborate string matching, coreference resolution tailored for dialogs
and a few other improvements. The method can correctly identify many values
that are not explicitly present in the utterance. On the final evaluation, our
method came in first among 7 competing teams and 24 entries. The F1-score
achieved by our method was 9 and 7 percentage points higher than that of the
runner-up for the utterance-level evaluation and for the subdialog-level
evaluation, respectively.

本研究使用字符串匹配、面向对话的共识消解和其他方法来跟踪对话状态，可正确标识很多在话语中未显式提到但存在的信息，并在 DSTC 4 比赛中表现优异。

大型本体的鲁棒对话状态跟踪

Robust Dialog State Tracking for Large Ontologies

Dialog state tracking is a key component of many modern dialog systems, most
of which are designed with a single, well-defined domain in mind. This paper
shows that dialog data drawn from different dialog domains can be used to train
a general belief tracking model which can operate across all of these domains,
exhibiting superior performance to each of the domain-specific models. We
propose a training procedure which uses out-of-domain data to initialise belief
tracking models for entirely new domains. This procedure leads to improvements
in belief tracking performance regardless of the amount of in-domain data
available for training the model.

该论文研究对话状态跟踪，提出通过跨域对话数据训练一般化信念跟踪模型，使用域外数据初始化跨域信念跟踪模型的训练流程，提高了信念跟踪的性能。