Context for language understanding by intelligent agents

McShane, Marjorie; Ниренбург, Сергей

doi:10.3233/ao-190216

Cited by 3 publications

(3 citation statements)

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“…Mcshane et al used domain knowledge to filter out domainrelated bilingual parallel corpora from large-scale general data when training translation models [12]. Ni reduces the impact of unregistered words on the overall translation performance of sentences through data generalization, improves the translation quality of unregistered words themselves, and uses a multi-coverage fusion model to improve the attention scoring mechanism to further alleviate overtranslation and neural MT in neural MT (missing translation problem) [13].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

2022

Journal of Environmental and Public Health

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Literary translation encompasses not only the communication of ideas across cultures but also the conversion of one word into another. The translation of Japanese literature will, therefore, undoubtedly convey this foreign culture. This study examines the semantic expression of words, the NN model for common sense reasoning, the method of building a common sense knowledge base, and the translation of Japanese literary language with an emphasis on the semantic expression and reasoning methods of natural language based on NN. The network structure, parameter processing, learning algorithm, and learning samples are all discussed in detail at the same time as the integrated NN model is presented. According to simulation results, this algorithm can achieve an accuracy of about 95% and a recall rate of about 96.5%. In addition to greatly enhancing the model’s flexibility and popularity, this approach successfully addresses the issue of information loss. It can offer some references for both the translation of literary language and the understanding of natural language.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

2022

Journal of Environmental and Public Health

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“…e algorithm uses the Chinese Albert pretraining model to obtain the dynamic word vector with context and then uses the conditional random field model which can effectively deal with the sequence annotation problem to complete the idiom meaning slot filling task [23]. At the same time, the multicore convolutional neural network is used for training, so that each datum has a corresponding intention label, so as to complete the intention recognition task [24].…”

Section: Design Intent Recognition Algorithmmentioning

confidence: 99%

Research on Spoken Language Understanding Based on Deep Learning

Yanli

2021

Scientific Programming

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Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.

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“…In "Context For Language Understanding by Intelligent Agents" McShane and Nirenburg (2019) illustrate a complete architecture aimed at providing language-endowed intelligent agents (LEIAs) with many kinds of context. This work mainly proposes a conceptual framework rather than an implemented system.…”

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confidence: 99%

Meaning in Context: Ontologically and linguistically motivated representations of objects and events

Basile

Caselli

Radicioni

2019

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Context is pervasive in all activities involving human beings as well as computer systems. It affects numerous aspects of our lives: how we understand the world, our communication, as well as the planning, carrying, and outcomes of activities. When focusing on understanding natural language, context plays a pivotal role that affects various facets ranging from the interpretation of speech signal to the identification of word meaning, composition of phrases and sentences as well as the intention of the discourse.Context impacts the conceptualisation of human experience. Objects and events assume different cognitive salience according to their context of occurrence, thus determining access to partial relevant information rather than to all information. Understanding a piece of natural language requires a continuous interaction between abstract conceptualisations and their actual use or occurrence in a specific communicative situation. A possible example to clarify this distinction could be that of an orange being passed between two children, and that of the same orange peeled on a table: in the former context the roundness of the object prevails over other features, traits, or aspects that characterise the concept of "the fruit orange". In the latter context, the edible features are those mostly relevant and conveyed. Similar mechanisms are at play with events. A well known example is that of "commercial events". Different surface forms (i.e., verbs) can be used to express related, if not the same, knowledge about the same scenario. For instance, the verbs buy and sell offer different interpretations of the same event, where none of them puts the focus on the object of the transaction, but rather on its participant. An extreme situation, in this case, is when different interpretations are imposed on the same event. For instance, a fatal shooting by the police can be interpreted as necessary self-defence (by the police officers) or as a murder (by the friends and family of the victim). Similar selectional mechanisms underlie figurative uses of word meanings, such as metonymy and metaphors among others, that intrinsically characterize the interface between knowledge and language -see the work by Gangemi et al. (2018) for a recent study on the ontological treatment of conceptual metaphors.

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Context for language understanding by intelligent agents

Cited by 3 publications

References 10 publications

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

[Retracted] Translation of Japanese Literature Language and Natural Language Environment Understanding Based on Artificial Neural Network

Research on Spoken Language Understanding Based on Deep Learning

Meaning in Context: Ontologically and linguistically motivated representations of objects and events

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