In this paper, we conduct a holistic exploration of the Universal
Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for
UDS parsing that decomposes the complex parsing task into semantically
appropriate subtasks. Our approach outperforms the prior models, while
significantly reducing inference time. We also incorporate syntactic
information and further optimized the architecture. Besides, different ways for
data augmentation are explored, which further improve the UDS Parsing. Lastly,
we conduct experiments to investigate the efficacy of ChatGPT in handling the
UDS task, revealing that it excels in attribute parsing but struggles in
relation parsing, and using ChatGPT for data augmentation yields suboptimal
results. Our code is available at this https URL

本文综合探讨了通用分解语义（UDS）解析，并引入了级联模型，将复杂的解析任务分解为语义上适当的子任务。我们的方法在优化了架构的同时，超越了先前的模型，并显著减少了推理时间。我们还结合了句法信息，进一步优化了架构。此外，我们还探索了不同的数据增强方法，进一步改进了 UDS 解析。最后，我们进行实验来研究 ChatGPT 在处理 UDS 任务方面的效果，发现它在属性解析方面表现出色，但在关系解析方面存在困难，而使用 ChatGPT 进行数据增强效果不佳。我们的代码可在此链接找到。

通用分解语义解析的整体性探索：架构、数据增强和 LLM 范式

Holistic Exploration on Universal Decompositional Semantic Parsing:  Architecture, Data Augmentation, and LLM Paradigm

In the 'Big Data' era, many real-world applications like search involve the
ranking problem for a large number of items. It is important to obtain
effective ranking results and at the same time obtain the results efficiently
in a timely manner for providing good user experience and saving computational
costs. Valuable prior research has been conducted for learning to efficiently
rank like the cascade ranking (learning) model, which uses a sequence of
ranking functions to progressively filter some items and rank the remaining
items. However, most existing research of learning to efficiently rank in
search is studied in a relatively small computing environments with simulated
user queries.
This paper presents novel research and thorough study of designing and
deploying a Cascade model in a Large-scale Operational E-commerce Search
application (CLOES), which deals with hundreds of millions of user queries per
day with hundreds of servers. The challenge of the real-world application
provides new insights for research: 1). Real-world search applications often
involve multiple factors of preferences or constraints with respect to user
experience and computational costs such as search accuracy, search latency,
size of search results and total CPU cost, while most existing search solutions
only address one or two factors; 2). Effectiveness of e-commerce search
involves multiple types of user behaviors such as click and purchase, while
most existing cascade ranking in search only models the click behavior. Based
on these observations, a novel cascade ranking model is designed and deployed
in an operational e-commerce search application. An extensive set of
experiments demonstrate the advantage of the proposed work to address multiple
factors of effectiveness, efficiency and user experience in the real-world
application.

在大规模电商搜索应用中，本文设计并部署了一种新型的级联排序模型，解决了实际搜索应用中多种用户体验、计算费用等约束条件的问题，有效提高了搜索效率和用户体验。

操作型电子商务搜索的级联排序

Cascade Ranking for Operational E-commerce Search

Most recommender systems recommend a list of items. The user examines the
list, from the first item to the last, and often chooses the first attractive
item and does not examine the rest. This type of user behavior can be modeled
by the cascade model. In this work, we study cascading bandits, an online
learning variant of the cascade model where the goal is to recommend $K$ most
attractive items from a large set of $L$ candidate items. We propose two
algorithms for solving this problem, which are based on the idea of linear
generalization. The key idea in our solutions is that we learn a predictor of
the attraction probabilities of items from their features, as opposing to
learning the attraction probability of each item independently as in the
existing work. This results in practical learning algorithms whose regret does
not depend on the number of items $L$. We bound the regret of one algorithm and
comprehensively evaluate the other on a range of recommendation problems. The
algorithm performs well and outperforms all baselines.

本研究提出了两种基于线性推广的算法来解决级联赌博问题，主要应用在推荐系统中。通过对算法的评估，发现本文算法表现较为良好，且优于所有基准算法。

基于级联赌博机的大规模推荐问题

Cascading Bandits for Large-Scale Recommendation Problems

A search engine usually outputs a list of $K$ web pages. The user examines
this list, from the first web page to the last, and chooses the first
attractive page. This model of user behavior is known as the cascade model. In
this paper, we propose cascading bandits, a learning variant of the cascade
model where the objective is to identify $K$ most attractive items. We
formulate our problem as a stochastic combinatorial partial monitoring problem.
We propose two algorithms for solving it, CascadeUCB1 and CascadeKL-UCB. We
also prove gap-dependent upper bounds on the regret of these algorithms and
derive a lower bound on the regret in cascading bandits. The lower bound
matches the upper bound of CascadeKL-UCB up to a logarithmic factor. We
experiment with our algorithms on several problems. The algorithms perform
surprisingly well even when our modeling assumptions are violated.

本文介绍了一种名为级联赌博的学习算法，用于在搜索引擎结果中识别出最有吸引力的 $K$ 个页面。研究将问题表述为随机组合式部分监控问题，并提出了两种算法，CascadeUCB1 和 CascadeKL-UCB，在实验中表现良好。