A common practice in metric learning is to train and test an embedding model
for each dataset. This dataset-specific approach fails to simulate real-world
scenarios that involve multiple heterogeneous distributions of data. In this
regard, we introduce a novel metric learning paradigm, called Universal Metric
Learning (UML), which learns a unified distance metric capable of capturing
relations across multiple data distributions. UML presents new challenges, such
as imbalanced data distribution and bias towards dominant distributions. To
address these challenges, we propose Parameter-efficient Universal Metric
leArning (PUMA), which consists of a pre-trained frozen model and two
additional modules, stochastic adapter and prompt pool. These modules enable to
capture dataset-specific knowledge while avoiding bias towards dominant
distributions. Additionally, we compile a new universal metric learning
benchmark with a total of 8 different datasets. PUMA outperformed the
state-of-the-art dataset-specific models while using about 69 times fewer
trainable parameters.

我们引入了一种名为通用度量学习（UML）的新型度量学习范式，它能够学习到能够捕捉多个数据分布关系的统一距离度量。通过使用预训练模型和两个额外的模块，即随机适配器和提示池，我们提出了一种名为参数高效通用度量学习（PUMA）的方法来解决不平衡数据分布和对主导分布的偏见等新挑战。此外，我们还编译了一个包含 8 个不同数据集的新通用度量学习基准。相较于特定数据集模型，PUMA 在使用大约 69 倍较少的可训练参数的情况下表现更好。

具有参数高效迁移学习的通用度量学习

Universal Metric Learning with Parameter-Efficient Transfer Learning

Existing neural machine translation (NMT) studies mainly focus on developing
dataset-specific models based on data from different tasks (e.g., document
translation and chat translation). Although the dataset-specific models have
achieved impressive performance, it is cumbersome as each dataset demands a
model to be designed, trained, and stored. In this work, we aim to unify these
translation tasks into a more general setting. Specifically, we propose a
``versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that
works with data from different tasks, and can translate well in multiple
settings simultaneously, and theoretically it can be as many as possible.
Through unified learning, UMLNMT is able to jointly train across multiple
tasks, implementing intelligent on-demand translation. On seven widely-used
translation tasks, including sentence translation, document translation, and
chat translation, our UMLNMT results in substantial improvements over
dataset-specific models with significantly reduced model deployment costs.
Furthermore, UMLNMT can achieve competitive or better performance than
state-of-the-art dataset-specific methods. Human evaluation and in-depth
analysis also demonstrate the superiority of our approach on generating diverse
and high-quality translations. Additionally, we provide a new genre translation
dataset about famous aphorisms with 186k Chinese->English sentence pairs.

本文提出了一个通用的深度神经机器翻译模型， Unified Model Learning for NMT (UMLNMT)，它能够在多种翻译任务中实现智能的即时翻译，相较于基于具体数据集训练的模型，该模型的性能有了显著的提升，部署成本也大大降低，并且在生成多样化、高质量的翻译方面表现优异。此外，作者也提供了一个关于名言警句的中英文句子翻译数据集。

多种神经机器翻译的统一模型学习

Unified Model Learning for Various Neural Machine Translation

Much of named entity recognition (NER) research focuses on developing
dataset-specific models based on data from the domain of interest, and a
limited set of related entity types. This is frustrating as each new dataset
requires a new model to be trained and stored. In this work, we present a
``versatile'' model -- the Prompting-based Unified NER system (PUnifiedNER) --
that works with data from different domains and can recognise up to 37 entity
types simultaneously, and theoretically it could be as many as possible. By
using prompt learning, PUnifiedNER is a novel approach that is able to jointly
train across multiple corpora, implementing intelligent on-demand entity
recognition. Experimental results show that PUnifiedNER leads to significant
prediction benefits compared to dataset-specific models with impressively
reduced model deployment costs. Furthermore, the performance of PUnifiedNER can
achieve competitive or even better performance than state-of-the-art
domain-specific methods for some datasets. We also perform comprehensive pilot
and ablation studies to support in-depth analysis of each component in
PUnifiedNER.

本文提出了一种基于提示学习技术的统一命名实体识别系统 (PUnifiedNER)，该系统能够在不同领域同时识别高达 37 种实体类型，相比基于特定数据集的模型，能够显著提高预测性能并大大降低模型部署成本。同时，在某些数据集上，PUnifiedNER 的性能甚至可以达到甚至超越领域特定方法的最高水平。