Language technologies that accurately model the dynamics of events must
perform commonsense reasoning. Existing work evaluating commonsense reasoning
focuses on making inferences about common, everyday situations. To instead
investigate the ability to model unusual, unexpected, and unlikely situations,
we explore the task of uncommonsense abductive reasoning. Given a piece of
context with an unexpected outcome, this task requires reasoning abductively to
generate a natural language explanation that makes the unexpected outcome more
likely in the context. To this end, we curate and release a new English
language corpus called UNcommonsense. We characterize the differences between
the performance of human explainers and the best performing large language
models, finding that model-enhanced human-written explanations achieve the
highest quality by trading off between specificity and diversity. Finally, we
experiment with several online imitation learning algorithms to train open and
accessible language models on this task. When compared with the vanilla
supervised fine-tuning approach, these methods consistently reduce lose rates
on both common and uncommonsense abductive reasoning judged by human
evaluators.

通过研究不寻常、意外和不太可能的情境，我们探索了非常识性推理的能力，发布了一个名为 UNcommonsense 的英语语料库，并比较人类解释者和最佳大型语言模型的表现差异，最终尝试了几种在线模仿学习算法来训练开放和易用的语言模型，在人工评估中，这些方法在常见和非常识性推理上一致降低了错误率。

非常规推理：关于非常规情况的归纳推理

UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations

Grounding temporal video segments described in natural language queries
effectively and efficiently is a crucial capability needed in
vision-and-language fields. In this paper, we deal with the fast video temporal
grounding (FVTG) task, aiming at localizing the target segment with high speed
and favorable accuracy. Most existing approaches adopt elaborately designed
cross-modal interaction modules to improve the grounding performance, which
suffer from the test-time bottleneck. Although several common space-based
methods enjoy the high-speed merit during inference, they can hardly capture
the comprehensive and explicit relations between visual and textual modalities.
In this paper, to tackle the dilemma of speed-accuracy tradeoff, we propose a
commonsense-aware cross-modal alignment (CCA) framework, which incorporates
commonsense-guided visual and text representations into a complementary common
space for fast video temporal grounding. Specifically, the commonsense concepts
are explored and exploited by extracting the structural semantic information
from a language corpus. Then, a commonsense-aware interaction module is
designed to obtain bridged visual and text features by utilizing the learned
commonsense concepts. Finally, to maintain the original semantic information of
textual queries, a cross-modal complementary common space is optimized to
obtain matching scores for performing FVTG. Extensive results on two
challenging benchmarks show that our CCA method performs favorably against
state-of-the-arts while running at high speed. Our code is available at
this https URL.

本文提出一种基于常识感知的跨模态对齐框架，通过从语料库中提取结构化语义信息，设计跨模态交互模块获得桥接式视觉和文本特征，并将其整合到共同的空间中进行快速视频时间定位。在两个具有挑战性的数据集上，证明该方法能够以高速运行，且表现优于现有的方法。

基于常识的快速视频时间对齐细节文本对齐学习

Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding

We revisit a pragmatic inference problem in dialog: understanding indirect
responses to questions. Humans can interpret 'I'm starving.' in response to
'Hungry?', even without direct cue words such as 'yes' and 'no'. In dialog
systems, allowing natural responses rather than closed vocabularies would be
similarly beneficial. However, today's systems are only as sensitive to these
pragmatic moves as their language model allows. We create and release the first
large-scale English language corpus 'Circa' with 34,268 (polar question,
indirect answer) pairs to enable progress on this task. The data was collected
via elaborate crowdsourcing, and contains utterances with yes/no meaning, as
well as uncertain, middle-ground, and conditional responses. We also present
BERT-based neural models to predict such categories for a question-answer pair.
We find that while transfer learning from entailment works reasonably,
performance is not yet sufficient for robust dialog. Our models reach 82-88%
accuracy for a 4-class distinction, and 74-85% for 6 classes.

本文关注了对话中的一种实用推断问题：理解对问题的间接回答。通过精心的众包，创建并发布了第一个大规模的英语语料库 'Circa'，并提出了基于 BERT 的神经模型来预测一对问题 - 答案的类别。结果表明，虽然从蕴涵的传递学习中可以得到合理的表现，但性能还不足以进行稳健的对话。我们的模型可以达到 4 类区分的 82-88％的准确性，6 类则为 74-85％。