Cross-lingual text classification leverages text classifiers trained in a
high-resource language to perform text classification in other languages with
no or minimal fine-tuning (zero/few-shots cross-lingual transfer). Nowadays,
cross-lingual text classifiers are typically built on large-scale, multilingual
language models (LMs) pretrained on a variety of languages of interest.
However, the performance of these models vary significantly across languages
and classification tasks, suggesting that the superposition of the language
modelling and classification tasks is not always effective. For this reason, in
this paper we propose revisiting the classic "translate-and-test" pipeline to
neatly separate the translation and classification stages. The proposed
approach couples 1) a neural machine translator translating from the targeted
language to a high-resource language, with 2) a text classifier trained in the
high-resource language, but the neural machine translator generates "soft"
translations to permit end-to-end backpropagation during fine-tuning of the
pipeline. Extensive experiments have been carried out over three cross-lingual
text classification datasets (XNLI, MLDoc and MultiEURLEX), with the results
showing that the proposed approach has significantly improved performance over
a competitive baseline.

该研究提出了一种基于经典的 “翻译和测试” 流程的跨语言文本分类方法，该方法将神经机器翻译器与高资源语言中的文本分类器相结合，通过端到端反向传播来进行微调，并在三个跨语言文本分类数据集上展示了显著的改进。

T3L：跨语言文本分类的翻译测试迁移学习

T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text  Classification

In recent years, Neural Machine Translator (NMT) has shown promise in
automatically editing source code. Typical NMT based code editor only considers
the code that needs to be changed as input and suggests developers with a
ranked list of patched code to choose from - where the correct one may not
always be at the top of the list. While NMT based code editing systems generate
a broad spectrum of plausible patches, the correct one depends on the
developers' requirement and often on the context where the patch is applied.
Thus, if developers provide some hints, using natural language, or providing
patch context, NMT models can benefit from them. As a proof of concept, in this
research, we leverage three modalities of information: edit location, edit code
context, commit messages (as a proxy of developers' hint in natural language)
to automatically generate edits with NMT models. To that end, we build MODIT, a
multi-modal NMT based code editing engine. With in-depth investigation and
analysis, we show that developers' hint as an input modality can narrow the
search space for patches and outperform state-of-the-art models to generate
correctly patched code in top-1 position.

该研究以三种信息模式来自动生成基于多模态神经机器翻译编辑代码。通过深入研究和分析显示：开发人员的提示作为输入模式可以缩小补丁搜索空间，并在 Top-1 位置生成正确的修补程序，从而胜过现有模型。

多模态学习编辑源代码

On Multi-Modal Learning of Editing Source Code

We propose to enhance the RNN decoder in a neural machine translator (NMT)
with external memory, as a natural but powerful extension to the state in the
decoding RNN. This memory-enhanced RNN decoder is called \textsc{MemDec}. At
each time during decoding, \textsc{MemDec} will read from this memory and write
to this memory once, both with content-based addressing. Unlike the unbounded
memory in previous work\cite{RNNsearch} to store the representation of source
sentence, the memory in \textsc{MemDec} is a matrix with pre-determined size
designed to better capture the information important for the decoding process
at each time step. Our empirical study on Chinese-English translation shows
that it can improve by $4.8$ BLEU upon Groundhog and $5.3$ BLEU upon on Moses,
yielding the best performance achieved with the same training set.

通过使用外部内存来增强神经机器翻译中 RNN 解码器的性能，我们提出了一种名为 MemDec 的基于内容寻址的 RNN 解码器。 通过在汉英翻译任务中的实证研究，我们发现 MemDec 相对于 Groundhog 和 Moses，可以提高 4.8 和 5.3 BLEU 分数，从而在相同的训练集上获得最佳性能。

神经机器翻译中的增强记忆解码器

Memory-enhanced Decoder for Neural Machine Translation

In this paper, we propose phraseNet, a neural machine translator with a
phrase memory which stores phrase pairs in symbolic form, mined from corpus or
specified by human experts. For any given source sentence, phraseNet scans the
phrase memory to determine the candidate phrase pairs and integrates tagging
information in the representation of source sentence accordingly. The decoder
utilizes a mixture of word-generating component and phrase-generating
component, with a specifically designed strategy to generate a sequence of
multiple words all at once. The phraseNet not only approaches one step towards
incorporating external knowledge into neural machine translation, but also
makes an effort to extend the word-by-word generation mechanism of recurrent
neural network. Our empirical study on Chinese-to-English translation shows
that, with carefully-chosen phrase table in memory, phraseNet yields 3.45 BLEU
improvement over the generic neural machine translator.

利用短语记忆，结合外部知识，采用生成多个词语序列的策略，提出一种神经机器翻译器 PhraseNet。在汉译英实验中，PhraseNet 相比普通神经机器翻译器，BLEU 值提高了 3.45。