Sequential fine-tuning and multi-task learning are methods aiming to
incorporate knowledge from multiple tasks; however, they suffer from
catastrophic forgetting and difficulties in dataset balancing. To address these
shortcomings, we propose AdapterFusion, a new two stage learning algorithm that
leverages knowledge from multiple tasks. First, in the knowledge extraction
stage we learn task specific parameters called adapters, that encapsulate the
task-specific information. We then combine the adapters in a separate knowledge
composition step. We show that by separating the two stages, i.e., knowledge
extraction and knowledge composition, the classifier can effectively exploit
the representations learned from multiple tasks in a non-destructive manner. We
empirically evaluate AdapterFusion on 16 diverse NLU tasks, and find that it
effectively combines various types of knowledge at different layers of the
model. We show that our approach outperforms traditional strategies such as
full fine-tuning as well as multi-task learning. Our code and adapters are
available at AdapterHub.ml.

AdapterFusion 是一种新型的学习算法，通过两阶段的任务学习过程实现了多任务学习与知识融合，并且可以有效地利用来自多项任务的知识表示，从而优于传统的全局微调和多任务学习方法。

AdapterFusion: 非破坏性任务组合用于迁移学习

AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Extracting structured knowledge from product profiles is crucial for various
applications in e-Commerce. State-of-the-art approaches for knowledge
extraction were each designed for a single category of product, and thus do not
apply to real-life e-Commerce scenarios, which often contain thousands of
diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge
extraction model that applies to thousands of product categories organized in a
hierarchical taxonomy. Through category conditional self-attention and
multi-task learning, our approach is both scalable, as it trains a single model
for thousands of categories, and effective, as it extracts category-specific
attribute values. Experiments on products from a taxonomy with 4,000 categories
show that TXtract outperforms state-of-the-art approaches by up to 10% in F1
and 15% in coverage across all categories.

本文介绍了 TXtract，一种适用于多个产品类别的结构化知识提取模型，使用层次分类法和多任务学习，提高了在 4000 个类别中，知识提取的效率和准确性。

TXtract: 面向数千个产品类别的分类知识提取

TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product  Categories

An increasing number of use cases require a timely extraction of non-trivial
knowledge from semantically annotated data streams, especially on the Web and
for the Internet of Things (IoT). Often, this extraction requires expressive
reasoning, which is challenging to compute on large streams. We propose Laser,
a new reasoner that supports a pragmatic, non-trivial fragment of the logic
LARS which extends Answer Set Programming (ASP) for streams. At its core, Laser
implements a novel evaluation procedure which annotates formulae to avoid the
re-computation of duplicates at multiple time points. This procedure, combined
with a judicious implementation of the LARS operators, is responsible for
significantly better runtimes than the ones of other state-of-the-art systems
like C-SPARQL and CQELS, or an implementation of LARS which runs on the ASP
solver Clingo. This enables the application of expressive logic-based reasoning
to large streams and opens the door to a wider range of stream reasoning use
cases.

Laser 是一种新的 reasoner，它支持扩展 Answer Set Programming（ASP）的 LARS 逻辑的实用、非平凡片段，并实现了一种新颖的评估过程，从而使表达式带注解，以避免在多个时间点重复计算，进而实现对大流的表达式逻辑推理，为流推理用例开启了更广泛的应用范围。