Each language has its own complex systems of word, phrase, and sentence
construction, the guiding principles of which are often summarized in grammar
descriptions for the consumption of linguists or language learners. However,
manual creation of such descriptions is a fraught process, as creating
descriptions which describe the language in "its own terms" without bias or
error requires both a deep understanding of the language at hand and
linguistics as a whole. We propose an automatic framework AutoLEX that aims to
ease linguists' discovery and extraction of concise descriptions of linguistic
phenomena. Specifically, we apply this framework to extract descriptions for
three phenomena: morphological agreement, case marking, and word order, across
several languages. We evaluate the descriptions with the help of language
experts and propose a method for automated evaluation when human evaluation is
infeasible.

AutoLEX 是一个自动框架，旨在帮助语言学家发现和提取语言现象的简洁描述，如形态一致性、格标记和词序。

AUTOLEX：一个自动语言探索框架

AUTOLEX: An Automatic Framework for Linguistic Exploration

Identifying factors that make certain languages harder to model than others
is essential to reach language equality in future Natural Language Processing
technologies. Free-order case-marking languages, such as Russian, Latin or
Tamil, have proved more challenging than fixed-order languages for the tasks of
syntactic parsing and subject-verb agreement prediction. In this work, we
investigate whether this class of languages is also more difficult to translate
by state-of-the-art Neural Machine Translation models (NMT). Using a variety of
synthetic languages and a newly introduced translation challenge set, we find
that word order flexibility in the source language only leads to a very small
loss of NMT quality, even though the core verb arguments become impossible to
disambiguate in sentences without semantic cues. The latter issue is indeed
solved by the addition of case marking. However, in medium- and low-resource
settings, the overall NMT quality of fixed-order languages remains unmatched.

探究自由语序语言和固定语序语言在自然语言处理技术中的表现差异，针对神经机器翻译技术的困难，提出了使用语态标记来解决歧义问题，但在中低资源情况下，固定语序的语言仍然表现出了更好的整体机器翻译质量。