The input to a neural sequence-to-sequence model is often determined by an
up-stream system, e.g. a word segmenter, part of speech tagger, or speech
recognizer. These up-stream models are potentially error-prone. Representing
inputs through word lattices allows making this uncertainty explicit by
capturing alternative sequences and their posterior probabilities in a compact
form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a
LatticeLSTM that is able to consume word lattices, and can be used as encoder
in an attentional encoder-decoder model. We integrate lattice posterior scores
into this architecture by extending the TreeLSTM's child-sum and forget gates
and introducing a bias term into the attention mechanism. We experiment with
speech translation lattices and report consistent improvements over baselines
that translate either the 1-best hypothesis or the lattice without posterior
scores.

研究使用基于树结构的神经网络模型作为编码器建立语音翻译模型，使用词汇图来代替输入，可以提高翻译准确性。

用于不确定输入的神经格点序列模型

Neural Lattice-to-Sequence Models for Uncertain Inputs

We describe a new framework for distilling information from word lattices to
improve the accuracy of speech recognition and obtain a more perspicuous
representation of a set of alternative hypotheses. In the standard MAP decoding
approach the recognizer outputs the string of words corresponding to the path
with the highest posterior probability given the acoustics and a language
model. However, even given optimal models, the MAP decoder does not necessarily
minimize the commonly used performance metric, word error rate (WER). We
describe a method for explicitly minimizing WER by extracting word hypotheses
with the highest posterior probabilities from word lattices. We change the
standard problem formulation by replacing global search over a large set of
sentence hypotheses with local search over a small set of word candidates. In
addition to improving the accuracy of the recognizer, our method produces a new
representation of the set of candidate hypotheses that specifies the sequence
of word-level confusions in a compact lattice format. We study the properties
of confusion networks and examine their use for other tasks, such as lattice
compression, word spotting, confidence annotation, and reevaluation of
recognition hypotheses using higher-level knowledge sources.

本文提出了一个新的方法，通过从单词网格中提取信息来提高语音识别的准确性，并获得一组更为清晰的备选假设，特别是在显式最小化字错误率的情况下，通过在小的单词候选集上局部搜索来改变标准问题公式，利用混淆网络来压缩网格，词汇定位和集合假设的重新评估。