Transformers in time-series analysis: A tutorial
Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …
Processing and Computer Vision. Recently, Transformers have been employed in various …
A survey on text-to-sql parsing: Concepts, methods, and future directions
Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is
to convert a natural language (NL) question to its corresponding structured query language …
to convert a natural language (NL) question to its corresponding structured query language …
H2o: Heavy-hitter oracle for efficient generative inference of large language models
Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the
structural property of the SQL queries, the seq2seq model takes the responsibility of parsing …
structural property of the SQL queries, the seq2seq model takes the responsibility of parsing …
PICARD: Parsing incrementally for constrained auto-regressive decoding from language models
Large pre-trained language models for textual data have an unconstrained output space; at
each decoding step, they can produce any of 10,000 s of sub-word tokens. When fine-tuned …
each decoding step, they can produce any of 10,000 s of sub-word tokens. When fine-tuned …
Deepnet: Scaling transformers to 1,000 layers
In this paper, we propose a simple yet effective method to stabilize extremely deep
Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify …
Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify …
LGESQL: line graph enhanced text-to-SQL model with mixed local and non-local relations
This work aims to tackle the challenging heterogeneous graph encoding problem in the text-
to-SQL task. Previous methods are typically node-centric and merely utilize different weight …
to-SQL task. Previous methods are typically node-centric and merely utilize different weight …
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
Sadga: Structure-aware dual graph aggregation network for text-to-sql
The Text-to-SQL task, aiming to translate the natural language of the questions into SQL
queries, has drawn much attention recently. One of the most challenging problems of Text-to …
queries, has drawn much attention recently. One of the most challenging problems of Text-to …
Few-shot text-to-sql translation using structure and content prompt learning
A common problem with adopting Text-to-SQL translation in database systems is poor
generalization. Specifically, when there is limited training data on new datasets, existing few …
generalization. Specifically, when there is limited training data on new datasets, existing few …