Children typically learn the meanings of nouns earlier than the meanings of
verbs. However, it is unclear whether this asymmetry is a result of complexity
in the visual structure of categories in the world to which language refers,
the structure of language itself, or the interplay between the two sources of
information. We quantitatively test these three hypotheses regarding early verb
learning by employing visual and linguistic representations of words sourced
from large-scale pre-trained artificial neural networks. Examining the
structure of both visual and linguistic embedding spaces, we find, first, that
the representation of verbs is generally more variable and less discriminable
within domain than the representation of nouns. Second, we find that if only
one learning instance per category is available, visual and linguistic
representations are less well aligned in the verb system than in the noun
system. However, in parallel with the course of human language development, if
multiple learning instances per category are available, visual and linguistic
representations become almost as well aligned in the verb system as in the noun
system. Third, we compare the relative contributions of factors that may
predict learning difficulty for individual words. A regression analysis reveals
that visual variability is the strongest factor that internally drives verb
learning, followed by visual-linguistic alignment and linguistic variability.
Based on these results, we conclude that verb acquisition is influenced by all
three sources of complexity, but that the variability of visual structure poses
the most significant challenge for verb learning.

通过大规模预训练的人工神经网络对单词进行视觉和语言表示，研究了早期动词学习的三个假设，并比较了影响困难度的因素。结果表明，视觉结构变异性是影响动词学习的最大挑战。

量化视觉、语言和视觉 - 语言复杂度在动词习得中的作用

Quantifying the Roles of Visual, Linguistic, and Visual-Linguistic  Complexity in Verb Acquisition

Recently, token-level adaptive training has achieved promising improvement in
machine translation, where the cross-entropy loss function is adjusted by
assigning different training weights to different tokens, in order to alleviate
the token imbalance problem. However, previous approaches only use static word
frequency information in the target language without considering the source
language, which is insufficient for bilingual tasks like machine translation.
In this paper, we propose a novel bilingual mutual information (BMI) based
adaptive objective, which measures the learning difficulty for each target
token from the perspective of bilingualism, and assigns an adaptive weight
accordingly to improve token-level adaptive training. This method assigns
larger training weights to tokens with higher BMI, so that easy tokens are
updated with coarse granularity while difficult tokens are updated with fine
granularity. Experimental results on WMT14 English-to-German and WMT19
Chinese-to-English demonstrate the superiority of our approach compared with
the Transformer baseline and previous token-level adaptive training approaches.
Further analyses confirm that our method can improve the lexical diversity.

本文提出了一种新的双语互信息（BMI）自适应目标函数，通过从双语视角度量每个目标单词的学习难度并相应地分配自适应权重来改进基于令牌级别的自适应训练，实验结果表明与变压器基线和以前的令牌级别自适应训练方法相比，本方法优越性明显，并能够改善词汇多样性。

基于双语互信息的自适应神经机器翻译训练

Bilingual Mutual Information Based Adaptive Training for Neural Machine  Translation

The use of deep reinforcement learning allows for high-dimensional state
descriptors, but little is known about how the choice of action representation
impacts the learning difficulty and the resulting performance. We compare the
impact of four different action parameterizations (torques, muscle-activations,
target joint angles, and target joint-angle velocities) in terms of learning
time, policy robustness, motion quality, and policy query rates. Our results
are evaluated on a gait-cycle imitation task for multiple planar articulated
figures and multiple gaits. We demonstrate that the local feedback provided by
higher-level action parameterizations can significantly impact the learning,
robustness, and quality of the resulting policies.

本文研究了用于高维状态描述的深度强化学习，并比较了不同的动作参数表示对学习难度、策略鲁棒性、运动质量和策略查询率的影响。从多个平面关节图形和多个步态的步态循环模仿任务的结果来看，本文表明较高级别的动作参数化所提供的本地反馈可以显著影响策略的学习、鲁棒性和质量。