Cross-lingual natural language inference is a fundamental problem in
cross-lingual language understanding. Many recent works have used prompt
learning to address the lack of annotated parallel corpora in XNLI. However,
these methods adopt discrete prompting by simply translating the templates to
the target language and need external expert knowledge to design the templates.
Besides, discrete prompts of human-designed template words are not trainable
vectors and can not be migrated to target languages in the inference stage
flexibly. In this paper, we propose a novel Soft prompt learning framework with
the Multilingual Verbalizer (SoftMV) for XNLI. SoftMV first constructs
cloze-style question with soft prompts for the input sample. Then we leverage
bilingual dictionaries to generate an augmented multilingual question for the
original question. SoftMV adopts a multilingual verbalizer to align the
representations of original and augmented multilingual questions into the same
semantic space with consistency regularization. Experimental results on XNLI
demonstrate that SoftMV can achieve state-of-the-art performance and
significantly outperform the previous methods under the few-shot and full-shot
cross-lingual transfer settings.

本文提出一种基于 SoftMV 框架与 Multilingual Verbalizer 的跨语种自然语言推理方法，通过生成软提示的填空式问题和使用双语词典，将原问题与扩增多语言问题的表示对齐到相同的语义空间中，并通过一致性正则化实现在 XNLI 数据集上的最新性能，特别是在少量样本和全量样本跨语种转移的情况下获得显着优于以前方法的结果。

通过多语言语言教师软提示提升跨语言自然语言推理

Enhancing Cross-lingual Natural Language Inference by Soft Prompting  with Multilingual Verbalizer

State-of-the-art multilingual systems rely on shared vocabularies that
sufficiently cover all considered languages. To this end, a simple and
frequently used approach makes use of subword vocabularies constructed jointly
over several languages. We hypothesize that such vocabularies are suboptimal
due to false positives (identical subwords with different meanings across
languages) and false negatives (different subwords with similar meanings). To
address these issues, we propose Subword Mapping and Anchoring across Languages
(SMALA), a method to construct bilingual subword vocabularies. SMALA extracts
subword alignments using an unsupervised state-of-the-art mapping technique and
uses them to create cross-lingual anchors based on subword similarities. We
demonstrate the benefits of SMALA for cross-lingual natural language inference
(XNLI), where it improves zero-shot transfer to an unseen language without
task-specific data, but only by sharing subword embeddings. Moreover, in neural
machine translation, we show that joint subword vocabularies obtained with
SMALA lead to higher BLEU scores on sentences that contain many false positives
and false negatives.

提出了一种名为 SMALA 的跨语言子词表构建方法，通过使用无监督的映射技术提取子词对齐并基于子词相似性创建跨语言锚点，可用于改善多语言自然语言推理和神经机器翻译等任务。

跨语言的子词映射和定位

Subword Mapping and Anchoring across Languages

We introduce an architecture to learn joint multilingual sentence
representations for 93 languages, belonging to more than 30 different families
and written in 28 different scripts. Our system uses a single BiLSTM encoder
with a shared BPE vocabulary for all languages, which is coupled with an
auxiliary decoder and trained on publicly available parallel corpora. This
enables us to learn a classifier on top of the resulting embeddings using
English annotated data only, and transfer it to any of the 93 languages without
any modification. Our experiments in cross-lingual natural language inference
(XNLI dataset), cross-lingual document classification (MLDoc dataset) and
parallel corpus mining (BUCC dataset) show the effectiveness of our approach.
We also introduce a new test set of aligned sentences in 112 languages, and
show that our sentence embeddings obtain strong results in multilingual
similarity search even for low-resource languages. Our implementation, the
pre-trained encoder and the multilingual test set are available at
this https URL

该研究介绍了一种以单个 BiLSTM 编码器为基础的多语言句子表示架构，其使用共享的 BPE 词汇表来学习 93 种语言的嵌入表示，并在公开可用的平行语料库上进行了训练。使用英文注释数据进行分类器训练，可将其转移至任何一种语言，且可以在跨语言自然语言推理、文档分类和平行语料库挖掘中取得良好效果。