A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

Ni, Pin; Li, Yuming; Li, Gangmin; Chang, Victor

doi:10.1145/3418208

Cited by 15 publications

(7 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model consists of three major components: a text encoder, an attention-based recurrent module, and an output layer. We first revisit the definition and notations about the widely used attention mechanism [10,11,12,13] and then introduce the details of our proposed ingredient parsing model.…”

Section: Methodsmentioning

confidence: 99%

Attention-based Ingredient Phrase Parser

Shi¹,

Ni²,

Wang³

et al. 2022

ESANN 2022 Proceedings

Self Cite

View full text Add to dashboard Cite

As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking, restaurant booking, and movie recommendation. Assisting users to cook is one of these tasks that are expected to be solved by intelligent assistants, where ingredients and their corresponding attributes, such as name, unit, and quantity, should be provided to users precisely and promptly. However, existing ingredient information scraped from the cooking website is in the unstructured form with huge variation in the lexical structure, for example, "1 garlic clove, crushed", and "1 (8 ounce) package cream cheese, softened", making it difficult to extract information exactly. To provide an engaged and successful conversational service to users for cooking tasks, we propose a new ingredient parsing model that can parse an ingredient phrase of recipes into the structure form with its corresponding attributes with over 0.93 F1-score. Experimental results show that our model achieves state-of-the-art performance on AllRecipes and Food.com datasets.

show abstract

Section: Methodsmentioning

confidence: 99%

Attention-based Ingredient Phrase Parser

Shi¹,

Ni²,

Wang³

et al. 2022

ESANN 2022 Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…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]. Wang et al used semantic role information to label nonterminal symbols in syntactic translation models, making translation rules more discriminative, and incorporating semantic information as a feature into existing translation models [14].…”

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

View full text Add to dashboard Cite

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.

show abstract

“…In addition, there is a lot of work on rule-based or deep learning-based chatbots or other NLP human–computer interaction components (Khilji et al, 2020 ; Ni et al, 2020c ; Reddy et al, 2020 ) and have provided many contributions to the development of this field. This includes a large number of novel NLP components, or related methods (Amith et al, 2019 ; Dai et al, 2019 ; Ni et al, 2019 , 2021a , 2021b ), which can also provide benefits for this area.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

Okhrati

Guan

et al. 2022

Inf Syst Front

Self Cite

View full text Add to dashboard Cite

Text-to-GraphQL (Text2GraphQL) is a task that converts the user's questions into Graph + QL (Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human–Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GraphQL task. This solution uses the Adapter pre-trained by “the linking of GraphQL schemas and the corresponding utterances” as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human–machine language translation task. In the study, the proposed Text2GraphQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GraphQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios.

show abstract

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

Cited by 15 publications

References 83 publications

Attention-based Ingredient Phrase Parser

Attention-based Ingredient Phrase Parser

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

Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

Contact Info

Product

Resources

About