In machine translation, a common problem is that the translation of certain
words even if translated can cause incomprehension of the target language
audience due to different cultural backgrounds. A solution to solve this
problem is to add explanations for these words. In a first step, we therefore
need to identify these words or phrases. In this work we explore techniques to
extract example explanations from a parallel corpus. However, the sparsity of
sentences containing words that need to be explained makes building the
training dataset extremely difficult. In this work, we propose a semi-automatic
technique to extract these explanations from a large parallel corpus.
Experiments on English->German language pair show that our method is able to
extract sentence so that more than 10% of the sentences contain explanation,
while only 1.9% of the original sentences contain explanations. In addition,
experiments on English->French and English->Chinese language pairs also show
similar conclusions. This is therefore an essential first automatic step to
create a explanation dataset. Furthermore we show that the technique is robust
for all three language pairs.

机器翻译中的一个常见问题是某些单词的翻译可能由于不同的文化背景导致目标语言受众难以理解。本研究探索从平行语料库中提取示例解释的技术，通过半自动的方法能够从大规模平行语料库中提取这些解释，为创建解释数据集提供了重要的自动化步骤。同时，通过实验证明该技术在三种语言对中均表现出鲁棒性。

机器翻译的针对特定受众的解释

Audience-specific Explanations for Machine Translation

Automatic legal judgment prediction and its explanation suffer from the
problem of long case documents exceeding tens of thousands of words, in
general, and having a non-uniform structure. Predicting judgments from such
documents and extracting their explanation becomes a challenging task, more so
on documents with no structural annotation. We define this problem as "scarce
annotated legal documents" and explore their lack of structural information and
their long lengths with a deep learning-based classification framework which we
call MESc; "Multi-stage Encoder-based Supervised with-clustering"; for judgment
prediction. Specifically, we divide a document into parts to extract their
embeddings from the last four layers of a custom fine-tuned Large Language
Model, and try to approximate their structure through unsupervised clustering.
Which we use in another set of transformer encoder layers to learn the
inter-chunk representations. We explore the adaptability of LLMs with
multi-billion parameters (GPT-Neo, and GPT-J) to legal texts and their
intra-domain(legal) transfer learning capacity. Alongside this, we compare
their performance with MESc and the impact of combining embeddings from their
last layers. For such hierarchical models, we also propose an explanation
extraction algorithm named ORSE; Occlusion sensitivity-based Relevant Sentence
Extractor;

对于长达数万字、结构不均匀的案例文件，自动法律判决预测及其解释存在困难。本研究使用称为 MESc 的深度学习分类框架，通过定义 “缺少结构化信息的法律文档” 问题，尝试从这些缺少结构标注的长文档中提取解释。同时，我们还研究了拥有数十亿参数的大型语言模型（GPT-Neo 和 GPT-J）在法律文本和领域内迁移学习方面的适应性，并将其性能与 MESc 及其最后层嵌入的组合进行了比较。对于这种层次模型，我们提出了一种名为 ORSE 的解释提取算法，即基于遮蔽敏感性的相关句子提取器。

大型非结构化法律文件中的分类与解释的分层神经框架

A Hierarchical Neural Framework for Classification and its Explanation  in Large Unstructured Legal Documents

We introduce Biased TextRank, a graph-based content extraction method
inspired by the popular TextRank algorithm that ranks text spans according to
their importance for language processing tasks and according to their relevance
to an input "focus." Biased TextRank enables focused content extraction for
text by modifying the random restarts in the execution of TextRank. The random
restart probabilities are assigned based on the relevance of the graph nodes to
the focus of the task. We present two applications of Biased TextRank: focused
summarization and explanation extraction, and show that our algorithm leads to
improved performance on two different datasets by significant ROUGE-N score
margins. Much like its predecessor, Biased TextRank is unsupervised, easy to
implement and orders of magnitude faster and lighter than current
state-of-the-art Natural Language Processing methods for similar tasks.

本文介绍了一种基于 Biased TextRank 算法的内容提取方法，可以实现针对性的文本提取，应用包括文本的聚焦摘要和解释提取，通过在执行 TextRank 时修改随机重新开始概率实现，与现有自然语言处理方法相比，不需要监督，更快速和轻量级