People often answer yes-no questions without explicitly saying yes, no, or
similar polar keywords. Figuring out the meaning of indirect answers is
challenging, even for large language models. In this paper, we investigate this
problem working with dialogues from multiple domains. We present new benchmarks
in three diverse domains: movie scripts, tennis interviews, and airline
customer service. We present an approach grounded on distant supervision and
blended training to quickly adapt to a new dialogue domain. Experimental
results show that our approach is never detrimental and yields F1 improvements
as high as 11-34%.

人们往往在回答是或否的问题时，没有明确地表达是或否或类似的极性关键词。本文通过研究来自多个领域的对话，并提出了三个不同领域的新基准：电影剧本，网球采访和航空公司客户服务。我们提出了一种基于远程监督和混合训练的方法，快速适应新的对话领域。实验结果表明，我们的方法从未受损，并能提高 F1 值高达 11-34%。

解读来自多个领域的对话中的是非问题答案

Interpreting Answers to Yes-No Questions in Dialogues from Multiple  Domains

In this paper, we propose a SOCratic model for Robots Approaching humans
based on TExt System (SOCRATES) focusing on the human search and approach based
on free-form textual description; the robot first searches for the target user,
then the robot proceeds to approach in a human-friendly manner. In particular,
textual descriptions are composed of appearance (e.g., wearing white shirts
with black hair) and location clues (e.g., is a student who works with robots).
We initially present a Human Search Socratic Model that connects large
pre-trained models in the language domain to solve the downstream task, which
is searching for the target person based on textual descriptions. Then, we
propose a hybrid learning-based framework for generating target-cordial robotic
motion to approach a person, consisting of a learning-from-demonstration module
and a knowledge distillation module. We validate the proposed searching module
via simulation using a virtual mobile robot as well as through real-world
experiments involving participants and the Boston Dynamics Spot robot.
Furthermore, we analyze the properties of the proposed approaching framework
with human participants based on the Robotic Social Attributes Scale (RoSAS)

提出一个以自然语言描述为基础的搜索和靠近目标用户的 SOCRATES 模型，通过一个大的预训练语言模型解决人类搜索问题，并使用混合学习的框架生成与人类亲和力的机器人运动，通过虚拟机器人和现实实验进行验证，并利用 Robotic 社会属性评定了方法的性能。

朝着基于文本的人类搜索和交互的智能机器狗

Towards Text-based Human Search and Approach with an Intelligent Robot Dog

This work proposes to analyse some keywords for bias analysis. For this, we
are using several NLP approaches and compare them based on their capability of
detecting keywords to analyse bias. The overall findings show that our proposed
approach gives comparable results with the state-of-the-art approaches on
different benchmark datasets.

本研究提出了一种基于 NLP 方法的关键词分析的偏差分析方法，实验结果表明该方法在不同基准数据集上具有与现有先进方法相当的效果。

偏见关键词

Keywords for Bias

We present a novel approach for training kernel Support Vector Machines,
establish learning runtime guarantees for our method that are better then those
of any other known kernelized SVM optimization approach, and show that our
method works well in practice compared to existing alternatives.

该研究提出了一种新的核支持向量机训练方法，建立了比现有方法更好的学习运行时保证，并且证明了该方法在实践中的有效性。