This paper connects a vector-based composition model to a formal semantics,
the Dependency-based Compositional Semantics (DCS). We show theoretical
evidence that the vector compositions in our model conform to the logic of DCS.
Experimentally, we show that vector-based composition brings a strong ability
to calculate similar phrases as similar vectors, achieving near
state-of-the-art on a wide range of phrase similarity tasks and relation
classification; meanwhile, DCS can guide building vectors for structured
queries that can be directly executed. We evaluate this utility on sentence
completion task and report a new state-of-the-art.

本文介绍了一个基于向量组合方法和 DCS 的形式语义模型。实验证明该模型在短语相似性和关系分类方面达到了近乎最新成果的能力，并在句子完成任务方面报告了一个新的最新成果。

学习语义和加性组合分布式表示

Learning Semantically and Additively Compositional Distributional  Representations

Suppose we want to build a system that answers a natural language question by
representing its semantics as a logical form and computing the answer given a
structured database of facts. The core part of such a system is the semantic
parser that maps questions to logical forms. Semantic parsers are typically
trained from examples of questions annotated with their target logical forms,
but this type of annotation is expensive.
Our goal is to learn a semantic parser from question-answer pairs instead,
where the logical form is modeled as a latent variable. Motivated by this
challenging learning problem, we develop a new semantic formalism,
dependency-based compositional semantics (DCS), which has favorable linguistic,
statistical, and computational properties. We define a log-linear distribution
over DCS logical forms and estimate the parameters using a simple procedure
that alternates between beam search and numerical optimization. On two standard
semantic parsing benchmarks, our system outperforms all existing
state-of-the-art systems, despite using no annotated logical forms.

研究使用问题 - 回答对来学习语义解析器，其中逻辑形式被建模为潜变量，并提出了一种新的语义形式 —— 基于依赖的组成语义学。该方法具有有利的语言学、统计和计算性质，并在两个标准语义解析基准测试中优于所有现有的最先进系统。