The need to compactly and robustly represent item-attribute relations arises
in many important tasks, such as faceted browsing and recommendation systems. A
popular machine learning approach for this task denotes that an item has an
attribute by a high dot-product between vectors for the item and attribute -- a
representation that is not only dense, but also tends to correct noisy and
incomplete data. While this method works well for queries retrieving items by a
single attribute (such as \emph{movies that are comedies}), we find that vector
embeddings do not so accurately support compositional queries (such as movies
that are comedies and British but not romances). To address these set-theoretic
compositions, this paper proposes to replace vectors with box embeddings, a
region-based representation that can be thought of as learnable Venn diagrams.
We introduce a new benchmark dataset for compositional queries, and present
experiments and analysis providing insights into the behavior of both. We find
that, while vector and box embeddings are equally suited to single attribute
queries, for compositional queries box embeddings provide substantial
advantages over vectors, particularly at the moderate and larger retrieval set
sizes that are most useful for users' search and browsing.

本篇论文提出了使用盒式嵌入进行组合查询的方法，相较于向量嵌入，盒式嵌入在中等和较大的检索集合大小方面为用户的搜索和浏览提供了更多的优势。

使用集合理论嵌入回答组合式查询

Answering Compositional Queries with Set-Theoretic Embeddings

Task oriented dialog systems typically first parse user utterances to
semantic frames comprised of intents and slots. Previous work on task oriented
intent and slot-filling work has been restricted to one intent per query and
one slot label per token, and thus cannot model complex compositional requests.
Alternative semantic parsing systems have represented queries as logical forms,
but these are challenging to annotate and parse. We propose a hierarchical
annotation scheme for semantic parsing that allows the representation of
compositional queries, and can be efficiently and accurately parsed by standard
constituency parsing models. We release a dataset of 44k annotated queries
(fb.me/semanticparsingdialog), and show that parsing models outperform
sequence-to-sequence approaches on this dataset.

本文提出了一种基于层级标注的语义解析方案，能够有效且准确地分析复杂的组合查询，同时发布了包含 44000 个标注查询的数据集，并证明此方案在标准句法分析模型上的表现优于序列到序列的方案。