国家重点基础研究发展计划(973)(2014CB340502)
国家社科基金青年项目(16CYY021)
江苏省社科基金青年项目(15YYC003)
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| Items | Types | |||||
| AGL of classic under finite state grammar | AGL without semantics of finite state grammar | AGL with controllable semantics of non-finite state grammar | AGL of “mini” natural language | |||
| Features | Generate sentences according to the nodes and paths on the state transition graph. | Generate sentences according to certain grammatical rules in natural language. | ||||
| Methods | The finite state grammar is used to construct a string for the participants to learn, and then judge if the new string conforms to the grammar rules previously learned implicitly. | Giving the artificial words some picture content or matching related scenes, and then use the artificial words to generate sentences according to the loop rules of finite-state grammar. | Using some word order (SVO, SOV, etc.) and morphological change (internal inflectional form and suffix) in natural languages, rules similar to natural language are formulated. | To study language learning by shaping natural language into artificial or semi-artificial micro language to construct a subset of natural language. | ||
| Goals | It is possible to study the pure grammar by eliminating the semantic factors in a language. | To capture the process of new grammar acquisition from other aspects of language learning and controlling. | ||||
| Process | The participants learn a series of strings, and then, based on the rules they learned, determine whether the rules involved in the new string conform to the rules they previously learned implicitly. | The participants were asked to identify some artificial words and combinations through the picture learning tasks, and then extract the abstract grammar rules learned in the picture word tasks. | The participants learn new sentences made up of artificial words in a meaningful context, and then they were asked to make grammatical judgments after reached a certain level of proficiency. | The participants were asked to learn a small number of words or sentences in a natural language and to observe how they responded to the rules of grammar after a short period of training. | ||
| Merits | Simple sequences are easy to learn and are suitable for studying the representation and degree of consciousness processing of acquired knowledge. | Withouting the interference of semantics, phonetics or pragmatics, the amount of previous language contact difference is excluded. | The introduction of word meaning promotes the learning of long-distance dependence between words on grammatical string. | The recursion performance based on the rules of natural language can make the newly learned rules produce the infinite sentences. | ||
| Demerits | There is no uniform stipulation on each characteristic index, the selection of symbol sequence is also random, and the nature of knowledge acquired in learning is controversial. | It can only be described as the transfer probability of adjacent elements between sequences. The words cannot have meaning or be used for generating coherent discourse. | The degree of dependence between artificial words is far less than that of natural language, their lexical meaning is obvious, but syntactic meaning is imperfect. | The amount of target language contact used in the experiment cannot be completely guaranteed, which affects the experimental results to some extent. | ||
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