Parsing and Grammatical Induction as Constraint Solving

动机：世界上大约有7，000种语言，其中包括仅加拿大就有60种不同的土著语言。它们中的许多都没有得到充分的研究，也没有得到计算和计算机化系统的支持：能够研究它们的语言学家数量不足，他们的大部分努力都投入到主流语言中。自动归纳语法的有效方法可以融入现代技术和教育工具，这将极大地影响语法归纳领域和相关的自然语言处理（NLP）领域，同时显着增加濒危语言的生存机会，从而带来社会经济潜力。最新技术水平：大多数语法归纳模型涉及概率（因此学习语法相当于从预先指定的模型族中选择模型）或机器学习的统计模型。在精度、可靠性和解释能力方面，这些模型不如基于规则和推理的模型，但它们通常可以通过对大量数据集的广泛搜索来克服经典NLP方法的不确定性，并且当重要的只是模拟图灵测试意义上的语言理解时，可以取得令人印象深刻的结果。一些模型使用一种语言的语言信息来描述另一种语言，而不是完全解决归纳问题，通常仅限于特定的任务，例如消除另一种语言的歧义，并且可能需要平行语料库。研究在最先进技术中的地位：相比之下，在我以前的NSERC资助的支持下，我和我的学生开发了一种新的模型，用于从已知语法中归纳未知语法，它不仅仅适用于特定任务，既不需要预先指定的模型家族，也不需要并行语料库，也不需要任何典型的机器学习模型。我们的模型使得有可能生成一个未充分研究的语言的语法，使用该语言中的代表性和正确的输入句子，连同与该输入相关的词典，所有这些都是相对于一个良好的研究语言的正确语法进行解析的。我们称我们的模型为子宫语法模型（WGM），因为它可以在适当的输入下生成新的语法，就像人类子宫可以生成所有种族一样。拟议研究：本提案的重点是巩固我们的实验WGM的结果到一个成熟的和可执行的计算语言学理论的分析和语法推理（长期目标），特别注重语义和语用解释（中期）。我们将微调这一理论的发展交错与测试台短期具体应用语法归纳（特别是围绕语言多样性保护在加拿大），解析本身，语言实验（作为测试替代语言的限制）。

Motivation: Roughly 7,000 languages are spoken in the world, including about 60 distinct indigenous languages in Canada alone. Many of them are under-studied and poorly supported by computational and computerized***systems: the number of linguists who can study them is insufficient and most of their efforts pour into mainstream languages. Efficient methods that automatically induce grammars that can be incorporated into modern technological and educational tools would greatly impact the Grammar Induction field and related Natural Language Processing (NLP) areas, while significantly increasing survival chances for endangered languages, with consequent socio-economic potential.******State-of-the-art: Most grammar induction models involve probabilities (so that learning a grammar amounts to selecting a model from a pre-specified model family) or statistical models of machine learning. With respect to precision, reliability and explanatory power, such models are inferior to rule and inference-based models, but they can often overcome the inflexibilities of classical NLP methods through extensive search on voluminous data sets, and can achieve impressive results when what matters is only to simulate linguistic understanding in the Turing test sense. A few models that use linguistic information from one language for the task of describing another language rather than addressing induction in full, are usually restricted to specific tasks, e.g. disambiguating the other language and may require parallel corpora.******Position of the research within the state-of-the-art: In contrast, with support from my previous NSERC grant, my students and I developed a novel model for inducing unknown grammars from known ones, which works for more than just specific tasks and needs neither a pre-specified model family, nor parallel corpora, nor any of the typical models of machine learning. Our model makes it possible to generate an under-studied language's grammar using representative and correct input sentences in that language, together with its lexicon relevant to that input, all of which is parsed with respect to the correct grammar of a well-studied language. We call our model the Womb Grammar Model (WGM) because it can generate new grammars given appropriate input, much as human wombs can generate all races.******Proposed research: The present proposal focuses on solidifying our experimental WGM results into a full-blown and executable computational linguistic theory of parsing and of grammatical inference (long term goal), with particular focus on semantic and pragmatic interpretation (medium-term). We shall fine-tune this theory by interleaving its development with test-bed short-term concrete applications to grammar induction (in particular, around linguistic diversity preservation in Canada), to parsing per se, and to linguistic experimentation (as testing alternative linguistic constraints).