This paper analyzes the features of monotonic translations, which follow the
word order of the source language, in simultaneous interpreting (SI). The word
order differences are one of the biggest challenges in SI, especially for
language pairs with significant structural differences like English and
Japanese. We analyzed the characteristics of monotonic translations using the
NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset
and found some grammatical structures that make monotonic translation difficult
in English-Japanese SI. We further investigated the features of monotonic
translations through evaluating the output from the existing speech translation
(ST) and simultaneous speech translation (simulST) models on NAIST
English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset as well
as on existing test sets. The results suggest that the existing SI-based test
set underestimates the model performance. We also found that the
monotonic-translation-based dataset would better evaluate simulST models, while
using an offline-based test set for evaluating simulST models underestimates
the model performance.

分析同传中顺译特征，在英日语言对中特别是英语和日语这种具有显著结构差异的语言对中，词序差异是同传的最大挑战之一。通过使用 NAIST 的英日顺译数据集对顺译特征进行了分析，并发现了一些在英日同传中使顺译困难的语法结构。通过评估现有语音翻译和同传语音翻译模型在 NAIST 的英日顺译数据集和现有测试集上的输出，进一步研究了顺译特征。结果表明，现有的基于同传的测试集低估了模型的性能。同时，发现基于顺译的数据集可以更好地评估同传模型，而使用离线测试集评估同传模型会低估模型的性能。

英日同传中的词语顺序：基于分块单调翻译的分析与评估

Word Order in English-Japanese Simultaneous Interpretation: Analyses and  Evaluation using Chunk-wise Monotonic Translation

Transfer learning aims to boost the learning on the target task leveraging
knowledge learned from other relevant tasks. However, when the source and
target are not closely related, the learning performance may be adversely
affected, a phenomenon known as negative transfer. In this paper, we
investigate the negative transfer in graph transfer learning, which is
important yet underexplored. We reveal that, unlike image or text, negative
transfer commonly occurs in graph-structured data, even when source and target
graphs share semantic similarities. Specifically, we identify that structural
differences significantly amplify the dissimilarities in the node embeddings
across graphs. To mitigate this, we bring a new insight: for semantically
similar graphs, although structural differences lead to significant
distribution shift in node embeddings, their impact on subgraph embeddings
could be marginal. Building on this insight, we introduce two effective yet
elegant methods, Subgraph Pooling (SP) and Subgraph Pooling++ (SP++), that
transfer subgraph-level knowledge across graphs. We theoretically analyze the
role of SP in reducing graph discrepancy and conduct extensive experiments to
evaluate its superiority under various settings. Our code and datasets are
available at: this https URL

本研究探讨图传递学习中的负迁移现象以及结构差异对图节点嵌入的影响，提出了两种有效的方法 Subgraph Pooling (SP) 和 Subgraph Pooling++ (SP++)，通过转移子图级别的知识来减少图之间的差异。

解决图上的负迁移

Tackling Negative Transfer on Graphs

Structural causal models (SCMs) are widely used in various disciplines to
represent causal relationships among variables in complex systems.
Unfortunately, the true underlying directed acyclic graph (DAG) structure is
often unknown, and determining it from observational or interventional data
remains a challenging task. However, in many situations, the end goal is to
identify changes (shifts) in causal mechanisms between related SCMs rather than
recovering the entire underlying DAG structure. Examples include analyzing gene
regulatory network structure changes between healthy and cancerous individuals
or understanding variations in biological pathways under different cellular
contexts. This paper focuses on identifying $\textit{functional}$ mechanism
shifts in two or more related SCMs over the same set of variables --
$\textit{without estimating the entire DAG structure of each SCM}$. Prior work
under this setting assumed linear models with Gaussian noises; instead, in this
work we assume that each SCM belongs to the more general class of nonlinear
additive noise models (ANMs). A key contribution of this work is to show that
the Jacobian of the score function for the $\textit{mixture distribution}$
allows for identification of shifts in general non-parametric functional
mechanisms. Once the shifted variables are identified, we leverage recent work
to estimate the structural differences, if any, for the shifted variables.
Experiments on synthetic and real-world data are provided to showcase the
applicability of this approach.

本文研究如何在不确定模型图结构的情况下，通过识别函数机制变化来比较两个或多个相关结构因果模型。我们提出使用非线性加性噪声模型对各模型进行建模，并证明了使用混合分布的分数函数的雅可比矩阵可以用于识别非参数函数机制中的变化。在确认变化的因素后，我们进一步使用其他方法来估计变化的结构差异，以此来帮助理解在不同环境下各个变量的差异。