Multi-hop textual question answering requires combining information from
multiple sentences. We focus on a natural setting where, unlike typical reading
comprehension, only partial information is provided with each question. The
model must retrieve and use additional knowledge to correctly answer the
question. To tackle this challenge, we develop a novel approach that explicitly
identifies the knowledge gap between a key span in the provided knowledge and
the answer choices. The model, GapQA, learns to fill this gap by determining
the relationship between the span and an answer choice, based on retrieved
knowledge targeting this gap. We propose jointly training a model to
simultaneously fill this knowledge gap and compose it with the provided partial
knowledge. On the OpenBookQA dataset, given partial knowledge, explicitly
identifying what's missing substantially outperforms previous approaches.

本文提出了一种名为 GapQA 的新方法，该方法可在具有部分信息的情况下，识别知识缺口并填补它，提高了多跳文本问答的准确性，并在 OpenBookQA 数据集上进行了测试。

基于知识间隙引导的多跳问答方法：寻找遗漏内容

What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question  Answering

Despite the remarkable recent progress, person re-identification (Re-ID)
approaches are still suffering from the failure cases where the discriminative
body parts are missing. To mitigate such cases, we propose a simple yet
effective Horizontal Pyramid Matching (HPM) approach to fully exploit various
partial information of a given person, so that correct person candidates can be
still identified even even some key parts are missing. Within the HPM, we make
the following contributions to produce a more robust feature representation for
the Re-ID task: 1) we learn to classify using partial feature representations
at different horizontal pyramid scales, which successfully enhance the
discriminative capabilities of various person parts; 2) we exploit average and
max pooling strategies to account for person-specific discriminative
information in a global-local manner. To validate the effectiveness of the
proposed HPM, extensive experiments are conducted on three popular benchmarks,
including Market-1501, DukeMTMC-ReID and CUHK03. In particular, we achieve mAP
scores of 83.1%, 74.5% and 59.7% on these benchmarks, which are the new
state-of-the-arts. Our code is available on Github

通过提出的 Horizontal Pyramid Matching 方法，我们可以在一定程度上减缓由于缺失身体部位而出现的识别失败情况，通过对不同的部分进行分类来增强各个人物部位的辨别能力，在全局和局部方面运用平均和最大池化策略来区分不同的人物信息，并在三个流行的基准测试中分别获得马记得 1501，DukeMTMC-ReID 和 CUHK03 的 mAP 分别为 83.1％，74.5％和 59.7％，从而达到新的最佳水平。

水平金字塔匹配的人员再识别

Horizontal Pyramid Matching for Person Re-identification

We study Matching and other related problems in a partial information setting
where the agents' utilities for being matched to other agents are hidden and
the mechanism only has access to ordinal preference information. Our model is
motivated by the fact that in many settings, agents cannot express the
numerical values of their utility for different outcomes, but are still able to
rank the outcomes in their order of preference. Specifically, we study problems
where the ground truth exists in the form of a weighted graph, and look to
design algorithms that approximate the true optimum matching using only the
preference orderings for each agent (induced by the hidden weights) as input.
If no restrictions are placed on the weights, then one cannot hope to do better
than the simple greedy algorithm, which yields a half optimal matching. Perhaps
surprisingly, we show that by imposing a little structure on the weights, we
can improve upon the trivial algorithm significantly: we design a
1.6-approximation algorithm for instances where the hidden weights obey the
metric inequality. Using our algorithms for matching as a black-box, we also
design new approximation algorithms for other closely related problems: these
include a a 3.2-approximation for the problem of clustering agents into equal
sized partitions, a 4-approximation algorithm for Densest k-subgraph, and a
2.14-approximation algorithm for Max TSP. These results are the first
non-trivial ordinal approximation algorithms for such problems, and indicate
that we can design robust algorithms even when we are agnostic to the precise
agent utilities.

研究了匹配和其他相关问题，提出一种只使用有限的偏序信息设计近似算法的模型，并证明当隐含权重满足度量不等式时，可以实现 1.6 倍近似度的匹配，以及利用匹配算法的黑箱设计了其他多个近似算法，推动了该问题的研究。

盲目、贪婪和随机：用于匹配和聚类的序数近似算法

Blind, Greedy, and Random: Ordinal Approximation Algorithms for Matching  and Clustering

We consider manipulation problems when the manipulator only has partial
information about the votes of the nonmanipulators. Such partial information is
described by an information set, which is the set of profiles of the
nonmanipulators that are indistinguishable to the manipulator. Given such an
information set, a dominating manipulation is a non-truthful vote that the
manipulator can cast which makes the winner at least as preferable (and
sometimes more preferable) as the winner when the manipulator votes truthfully.
When the manipulator has full information, computing whether or not there
exists a dominating manipulation is in P for many common voting rules (by known
results). We show that when the manipulator has no information, there is no
dominating manipulation for many common voting rules. When the manipulator's
information is represented by partial orders and only a small portion of the
preferences are unknown, computing a dominating manipulation is NP-hard for
many common voting rules. Our results thus throw light on whether we can
prevent strategic behavior by limiting information about the votes of other
voters.

本研究探讨了当操纵者只有部分投票者的信息时的操纵问题，并研究了在不同投票规则下计算占优操纵的难度，同时探讨了如何限制其他投票者信息以防止投票的策略行为。