Canonical morphological segmentation is the process of analyzing words into
the standard (aka underlying) forms of their constituent morphemes. This is a
core task in language documentation, and NLP systems have the potential to
dramatically speed up this process. But in typical language documentation
settings, training data for canonical morpheme segmentation is scarce, making
it difficult to train high quality models. However, translation data is often
much more abundant, and, in this work, we present a method that attempts to
leverage this data in the canonical segmentation task. We propose a
character-level sequence-to-sequence model that incorporates representations of
translations obtained from pretrained high-resource monolingual language models
as an additional signal. Our model outperforms the baseline in a super-low
resource setting but yields mixed results on training splits with more data.
While further work is needed to make translations useful in higher-resource
settings, our model shows promise in severely resource-constrained settings.

标准词素分析是将单词分析为其组成形态素的标准（即底层）形式的过程。本文提出了一种利用先前经过训练的高资源单语言模型获得的翻译的字符级序列到序列模型来改善标准分词任务的方法，取得了在无资源情况下超越基准的效果，但在训练数据更多的情况下结果不稳定。尽管在高资源环境中使用翻译仍需进一步研究，但我们的模型在资源极度有限的情况下展现出希望。

TAMS：基于翻译的形态分割

TAMS: Translation-Assisted Morphological Segmentation

Advanced neural machine translation (NMT) models generally implement encoder
and decoder as multiple layers, which allows systems to model complex functions
and capture complicated linguistic structures. However, only the top layers of
encoder and decoder are leveraged in the subsequent process, which misses the
opportunity to exploit the useful information embedded in other layers. In this
work, we propose to simultaneously expose all of these signals with layer
aggregation and multi-layer attention mechanisms. In addition, we introduce an
auxiliary regularization term to encourage different layers to capture diverse
information. Experimental results on widely-used WMT14 English-German and WMT17
Chinese-English translation data demonstrate the effectiveness and universality
of the proposed approach.

本文提出了一种新的神经机器翻译方法，采用层聚合和多层注意力机制，通过引入辅助正则化项促进不同层捕获多样化信息，实验结果表明该方法在 WMT14 英德和 WMT17 中英数据上具有普适性与有效性。

利用深度表示进行神经机器翻译

Exploiting Deep Representations for Neural Machine Translation

This work exploits translation data as a source of semantically relevant
learning signal for models of word representation. In particular, we exploit
equivalence through translation as a form of distributed context and jointly
learn how to embed and align with a deep generative model. Our EmbedAlign model
embeds words in their complete observed context and learns by marginalisation
of latent lexical alignments. Besides, it embeds words as posterior probability
densities, rather than point estimates, which allows us to compare words in
context using a measure of overlap between distributions (e.g. KL divergence).
We investigate our model's performance on a range of lexical semantics tasks
achieving competitive results on several standard benchmarks including natural
language inference, paraphrasing, and text similarity.

使用翻译数据作为词表示模型的语义相关学习信号的源，利用翻译中的等价性作为分布式上下文进行联合嵌入和对齐，通过概率归一化的隐变量词汇对齐实现嵌入词，并比较上下文中的词的重叠度。应用于多个语义任务上获得了良好的性能表现。