{"title":"拓扑关系感知变压器","authors":"Nathan Manzambi Ndongala","doi":"10.21522/tijar.2014.11.01.art015","DOIUrl":null,"url":null,"abstract":"We present a Topological Relation Aware Transformer (T-RAT), a specialized head transformer to open sets, an element of the topology τ generated by the set S, the set of all pre-existing relations between input tokens of the model. From this topological space (S, τ), we present the way to spread each open set to one head of our Transformer. T-RAT improves exact match accuracy in Text-To-SQL challenge (62.09%) without any enhancement of large language models compared to the baseline models RAT-SQL (57.2%) and Light RAT-SQL (60.25%). Keywords: Deep learning, Natural Language Processing, Neural Semantic Parsing, Relation Aware Transformer, RAT-SQL, Text-To-SQL Transformer.","PeriodicalId":22213,"journal":{"name":"TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH","volume":"711 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Topological Relation Aware Transformer\",\"authors\":\"Nathan Manzambi Ndongala\",\"doi\":\"10.21522/tijar.2014.11.01.art015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a Topological Relation Aware Transformer (T-RAT), a specialized head transformer to open sets, an element of the topology τ generated by the set S, the set of all pre-existing relations between input tokens of the model. From this topological space (S, τ), we present the way to spread each open set to one head of our Transformer. T-RAT improves exact match accuracy in Text-To-SQL challenge (62.09%) without any enhancement of large language models compared to the baseline models RAT-SQL (57.2%) and Light RAT-SQL (60.25%). Keywords: Deep learning, Natural Language Processing, Neural Semantic Parsing, Relation Aware Transformer, RAT-SQL, Text-To-SQL Transformer.\",\"PeriodicalId\":22213,\"journal\":{\"name\":\"TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH\",\"volume\":\"711 \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21522/tijar.2014.11.01.art015\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21522/tijar.2014.11.01.art015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
我们提出了拓扑关系感知变换器(T-RAT),这是一个专门用于开放集的头部变换器,开放集是由集合 S 生成的拓扑结构 τ 中的一个元素,集合 S 是模型输入标记之间所有已有关系的集合。从这个拓扑空间(S,τ)中,我们提出了将每个开放集扩散到转换器的一个头部的方法。与基线模型 RAT-SQL (57.2%) 和 Light RAT-SQL (60.25%) 相比,T-RAT 提高了 Text-To-SQL 挑战赛中的精确匹配准确率(62.09%),而无需增强大型语言模型。关键词深度学习、自然语言处理、神经语义解析、关系感知转换器、RAT-SQL、Text-To-SQL 转换器。
We present a Topological Relation Aware Transformer (T-RAT), a specialized head transformer to open sets, an element of the topology τ generated by the set S, the set of all pre-existing relations between input tokens of the model. From this topological space (S, τ), we present the way to spread each open set to one head of our Transformer. T-RAT improves exact match accuracy in Text-To-SQL challenge (62.09%) without any enhancement of large language models compared to the baseline models RAT-SQL (57.2%) and Light RAT-SQL (60.25%). Keywords: Deep learning, Natural Language Processing, Neural Semantic Parsing, Relation Aware Transformer, RAT-SQL, Text-To-SQL Transformer.