With the rise of telemedicine, the task of developing Dialogue Systems for
Medical Diagnosis (DSMD) has received much attention in recent years. Different
from early researches that needed to rely on extra human resources and
expertise to help construct the system, recent researches focused on how to
build DSMD in a purely data-driven manner. However, the previous data-driven
DSMD methods largely overlooked the system interpretability, which is critical
for a medical application, and they also suffered from the data sparsity issue
at the same time. In this paper, we explore how to bring interpretability to
data-driven DSMD. Specifically, we propose a more interpretable decision
process to implement the dialogue manager of DSMD by reasonably mimicking real
doctors' inquiry logics, and we devise a model with highly transparent
components to conduct the inference. Moreover, we collect a new DSMD dataset,
which has a much larger scale, more diverse patterns and is of higher quality
than the existing ones. The experiments show that our method obtains 7.7%,
10.0%, 3.0% absolute improvement in diagnosis accuracy respectively on three
datasets, demonstrating the effectiveness of its rational decision process and
model design. Our codes and the GMD-12 dataset are available at
this https URL.

本文研究在医疗诊断中，如何在数据驱动的医疗诊断对话系统中加入可解释性和适当的决策逻辑，并通过大规模的数据集和透明组件的设计使诊断准确率分别得到 7.7％，10.0％和 3.0％的提高。

利用可解释问询逻辑建立医学诊断智能代理：从 “我的鼻子流鼻涕” 到 “你有咳嗽吗？

"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics

Many large-scale applications amount to finding relevant results from an
enormous output space of potential candidates. For example, finding the best
matching product from a large catalog or suggesting related search phrases on a
search engine. The size of the output space for these problems can range from
millions to billions, and can even be infinite in some applications. Moreover,
training data is often limited for the long-tail items in the output space.
Fortunately, items in the output space are often correlated thereby presenting
an opportunity to alleviate the data sparsity issue. In this paper, we propose
the Prediction for Enormous and Correlated Output Spaces (PECOS) framework, a
versatile and modular machine learning framework for solving prediction
problems for very large output spaces, and apply it to the eXtreme Multilabel
Ranking (XMR) problem: given an input instance, find and rank the most relevant
items from an enormous but fixed and finite output space. We propose a three
phase framework for PECOS: (i) in the first phase, PECOS organizes the output
space using a semantic indexing scheme, (ii) in the second phase, PECOS uses
the indexing to narrow down the output space by orders of magnitude using a
machine learned matching scheme, and (iii) in the third phase, PECOS ranks the
matched items using a final ranking scheme. The versatility and modularity of
PECOS allows for easy plug-and-play of various choices for the indexing,
matching, and ranking phases. We also develop very fast inference procedures
which allow us to perform XMR predictions in real time; for example, inference
takes less than 1 millisecond per input on the dataset with 2.8 million labels.
The PECOS software is available at this https URL

我们提出了一个名为 “PEXOS” 的机器学习框架，可在解决预测大型输出空间问题时提高数据密集程度。该框架通过三个阶段，即利用语义索引方案组织输出，通过机器学习匹配方案缩小输出范围，通过最终排名方案对匹配项目进行排名。此框架可根据需要进行配置以适应不同的索引、匹配和排名方式，可用于解决 eXtreme Multilabel Ranking 等问题。

PECOS：针对巨大和相关输出空间的预测

PECOS: Prediction for Enormous and Correlated Output Spaces

Knowledge graphs (KGs) have proven to be effective for high-quality
recommendation, where the connectivities between users and items provide rich
and complementary information to user-item interactions. Most existing methods,
however, are insufficient to exploit the KGs for capturing user preferences, as
they either represent the user-item connectivities via paths with limited
expressiveness or implicitly model them by propagating information over the
entire KG with inevitable noise. In this paper, we design a novel hierarchical
attentive knowledge graph embedding (HAKG) framework to exploit the KGs for
effective recommendation. Specifically, HAKG first extracts the expressive
subgraphs that link user-item pairs to characterize their connectivities, which
accommodate both the semantics and topology of KGs. The subgraphs are then
encoded via a hierarchical attentive subgraph encoding to generate effective
subgraph embeddings for enhanced user preference prediction. Extensive
experiments show the superiority of HAKG against state-of-the-art
recommendation methods, as well as its potential in alleviating the data
sparsity issue.

本文提出了一种新的分层注意力知识图嵌入框架（HAKG），用于捕捉用户偏好，并在缓解数据稀疏性问题方面表现出优越性。

个性化推荐的分层关注知识图谱嵌入

Hierarchical Attentive Knowledge Graph Embedding for Personalized  Recommendation

In aspect-based sentiment analysis, most existing methods either focus on
aspect/opinion terms extraction or aspect terms categorization. However, each
task by itself only provides partial information to end users. To generate more
detailed and structured opinion analysis, we propose a finer-grained problem,
which we call category-specific aspect and opinion terms extraction. This
problem involves the identification of aspect and opinion terms within each
sentence, as well as the categorization of the identified terms. To this end,
we propose an end-to-end multi-task attention model, where each task
corresponds to aspect/opinion terms extraction for a specific category. Our
model benefits from exploring the commonalities and relationships among
different tasks to address the data sparsity issue. We demonstrate its
state-of-the-art performance on three benchmark datasets.

提出细化问题 -- 分类特定方面和意见词提取，并使用多任务注意力模型解决数据稀疏性问题，在三个基准数据集上展示出其最先进的性能。