We consider an unanswered question in the discourse processing community: why
do relation classifiers trained on explicit examples (with connectives removed)
perform poorly in real implicit scenarios? Prior work claimed this is due to
linguistic dissimilarity between explicit and implicit examples but provided no
empirical evidence. In this study, we show that one cause for such failure is a
label shift after connectives are eliminated. Specifically, we find that the
discourse relations expressed by some explicit instances will change when
connectives disappear. Unlike previous work manually analyzing a few examples,
we present empirical evidence at the corpus level to prove the existence of
such shift. Then, we analyze why label shift occurs by considering factors such
as the syntactic role played by connectives, ambiguity of connectives, and
more. Finally, we investigate two strategies to mitigate the label shift:
filtering out noisy data and joint learning with connectives. Experiments on
PDTB 2.0, PDTB 3.0, and the GUM dataset demonstrate that classifiers trained
with our strategies outperform strong baselines.

我们的研究揭示了，关系分类器在去除连接词后通过明确的示例进行训练在隐含场景中表现不佳的原因之一是标签偏移。通过在语料库级别提供经验证据，我们证明了标签偏移的存在，并通过考虑连接词的句法角色、连接词的歧义性等因素，分析了标签偏移发生的原因。最后，我们研究了两种缓解标签偏移的策略：过滤噪声数据和与连接词的联合学习。在 PDTB 2.0、PDTB 3.0 和 GUM 数据集上的实验表明，使用我们的策略训练的分类器优于强基线模型。

什么导致显式到隐式篇章联系识别失败？

What Causes the Failure of Explicit to Implicit Discourse Relation  Recognition?

Predicting a scene graph that captures visual entities and their interactions
in an image has been considered a crucial step towards full scene
comprehension. Recent scene graph generation (SGG) models have shown their
capability of capturing the most frequent relations among visual entities.
However, the state-of-the-art results are still far from satisfactory, e.g.
models can obtain 31% in overall recall R@100, whereas the likewise important
mean class-wise recall mR@100 is only around 8% on Visual Genome (VG). The
discrepancy between R and mR results urges to shift the focus from pursuing a
high R to a high mR with a still competitive R. We suspect that the observed
discrepancy stems from both the annotation bias and sparse annotations in VG,
in which many visual entity pairs are either not annotated at all or only with
a single relation when multiple ones could be valid. To address this particular
issue, we propose a novel SGG training scheme that capitalizes on self-learned
knowledge. It involves two relation classifiers, one offering a less biased
setting for the other to base on. The proposed scheme can be applied to most of
the existing SGG models and is straightforward to implement. We observe
significant relative improvements in mR (between +6.6% and +20.4%) and
competitive or better R (between -2.4% and 0.3%) across all standard SGG tasks.

提出了一种基于自学知识的场景图生成模型训练方案，使用两个关系分类器减少标注偏差和稀疏标注造成的影响，并在多个标准场景图生成任务中观察到了显著的相对改进。