拓扑关系感知变压器

Nathan Manzambi Ndongala
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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。
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Topological Relation Aware Transformer
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.
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