We introduce an architecture, the Tensor Product Recurrent Network (TPRN). In
our application of TPRN, internal representations learned by end-to-end
optimization in a deep neural network performing a textual question-answering
(QA) task can be interpreted using basic concepts from linguistic theory. No
performance penalty need be paid for this increased interpretability: the
proposed model performs comparably to a state-of-the-art system on the SQuAD QA
task. The internal representation which is interpreted is a Tensor Product
Representation: for each input word, the model selects a symbol to encode the
word, and a role in which to place the symbol, and binds the two together. The
selection is via soft attention. The overall interpretation is built from
interpretations of the symbols, as recruited by the trained model, and
interpretations of the roles as used by the model. We find support for our
initial hypothesis that symbols can be interpreted as lexical-semantic word
meanings, while roles can be interpreted as approximations of grammatical roles
(or categories) such as subject, wh-word, determiner, etc. Fine-grained
analysis reveals specific correspondences between the learned roles and parts
of speech as assigned by a standard tagger (Toutanova et al. 2003), and finds
several discrepancies in the model's favor. In this sense, the model learns
significant aspects of grammar, after having been exposed solely to
linguistically unannotated text, questions, and answers: no prior linguistic
knowledge is given to the model. What is given is the means to build
representations using symbols and roles, with an inductive bias favoring use of
these in an approximately discrete manner.

使用承载基本语言学概念的 Tensor Product Recurrent Network (TPRN) 架构，在深度神经网络 QA 任务中学习内部表示，并成功解释了单词的语义含义与语法角色，从而证明了模型可以从未标注的文本中学习语法。