We present labeled morphological segmentation, an alternative view of
morphological processing that unifies several tasks. From an annotation
standpoint, we additionally introduce a new hierarchy of morphotactic tagsets.
Finally, we develop \modelname, a discriminative morphological segmentation
system that, contrary to previous work, explicitly models morphotactics. We
show that \textsc{chipmunk} yields improved performance on three tasks for all
six languages: (i) morphological segmentation, (ii) stemming and (iii)
morphological tag classification. On morphological segmentation, our method
shows absolute improvements of 2--6 points $F_1$ over the baseline.

我们提出了有标签的形态学分割，这是一种统一几个任务的形态处理的替代视角。从标注角度来看，我们还引入了一种新的形态句法标签集的层次结构。最后，我们开发了	extsc {modelname}，这是一个判别性的形态学分割系统，与之前的工作相反，它明确地建模了形态句法。我们展示了	extsc {modelname} 在六种语言的三个任务上的改进表现：（i）形态学分割，（ii）词干提取和（iii）形态学标签分类。在形态学分割上，我们的方法相对于基准线有 2-6 个 $F_1$ 的绝对改进。

带半马尔可夫模型的标注形态学分割

Labeled Morphological Segmentation with Semi-Markov Models

Detecting dialogue breakdown in real time is critical for conversational AI
systems, because it enables taking corrective action to successfully complete a
task. In spoken dialog systems, this breakdown can be caused by a variety of
unexpected situations including high levels of background noise, causing STT
mistranscriptions, or unexpected user flows. In particular, industry settings
like healthcare, require high precision and high flexibility to navigate
differently based on the conversation history and dialogue states. This makes
it both more challenging and more critical to accurately detect dialog
breakdown. To accurately detect breakdown, we found it requires processing
audio inputs along with downstream NLP model inferences on transcribed text in
real time. In this paper, we introduce a Multimodal Contextual Dialogue
Breakdown (MultConDB) model. This model significantly outperforms other known
best models by achieving an F1 of 69.27.

实时检测对话中的中断对于会话型人工智能系统至关重要，因为它能采取纠正措施以成功完成任务。本文介绍了一种多模态上下文对话中断模型（MultConDB），该模型通过在实时转录文本上进行音频输入处理和下游自然语言处理模型推理来精确检测对话中断，并获得了 69.27 的 F1 分数，显著优于其他已知最佳模型。

多模态环境下对话中断检测

Multimodal Contextual Dialogue Breakdown Detection for Conversational AI  Models

The performance of a binary classifier is described by a confusion matrix
with four entries: the number of true positives (TP), true negatives (TN),
false positives (FP), and false negatives (FN).
The Matthew's Correlation Coefficient (MCC), F1, and Fowlkes--Mallows (FM)
scores are scalars that summarize a confusion matrix. Both the F1 and FM scores
are based on only three of the four entries in the confusion matrix (they
ignore TN). In contrast, the MCC takes into account all four entries of the
confusion matrix and thus can be seen as providing a more representative
picture.
However, in object detection problems, measuring the number of true negatives
is so large it is often intractable. Thus we ask, what happens to the MCC as
the number of true negatives approaches infinity? This paper provides insight
into the relationship between the MCC and FM score by proving that the
FM-measure is equal to the limit of the MCC as the number of true negatives
approaches infinity.

本文研究二元分类器的表现，通过混淆矩阵描述该分类器的真阳性、真阴性、假阳性和假阴性，提出了 MCC，F1 和 FM 得分来总结混淆矩阵信息，其中 MCC 综合考虑整个混淆矩阵；作者证明了，在目标检测问题中有时候计算真阴性的数量太大，难以度量，因此提出仅考虑真阳性、假阳性和假阴性的 FM 和 F1 分数，同时证明 FM 和 MCC 实际上是等价的。