There has been considerable attention devoted to models that learn to jointly
infer an expression's syntactic structure and its semantics. Yet,
\citet{NangiaB18} has recently shown that the current best systems fail to
learn the correct parsing strategy on mathematical expressions generated from a
simple context-free grammar. In this work, we present a recursive model
inspired by \newcite{ChoiYL18} that reaches near perfect accuracy on this task.
Our model is composed of two separated modules for syntax and semantics. They
are cooperatively trained with standard continuous and discrete optimization
schemes. Our model does not require any linguistic structure for supervision
and its recursive nature allows for out-of-domain generalization with little
loss in performance. Additionally, our approach performs competitively on
several natural language tasks, such as Natural Language Inference or Sentiment
Analysis.

本研究提出了一种递归模型，能够在不需要任何语言结构监督的情况下，通过两个单独的语法和语义模块进行协同训练，达到几乎完美的数学表达式分析精度，并可在不同领域进行泛化，并在自然语言推理和情感分析等多个自然语言任务中表现出竞争性。

不交叉的语法和语义的合作学习

Cooperative Learning of Disjoint Syntax and Semantics

The chain-structured long short-term memory (LSTM) has showed to be effective
in a wide range of problems such as speech recognition and machine translation.
In this paper, we propose to extend it to tree structures, in which a memory
cell can reflect the history memories of multiple child cells or multiple
descendant cells in a recursive process. We call the model S-LSTM, which
provides a principled way of considering long-distance interaction over
hierarchies, e.g., language or image parse structures. We leverage the models
for semantic composition to understand the meaning of text, a fundamental
problem in natural language understanding, and show that it outperforms a
state-of-the-art recursive model by replacing its composition layers with the
S-LSTM memory blocks. We also show that utilizing the given structures is
helpful in achieving a performance better than that without considering the
structures.

本文提出了一种基于树结构的长短时记忆网络模型 S-LSTM，用于实现文本语义组合和自然语言理解，比现有基于递归的模型效果更佳。