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Found 11 papers, 1 papers with code
ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps
One of the main reasons for this plateau is the lack of readily available geographic knowledge in generic PTMs.
Monocular Camera Localization for Automated Vehicles Using Image Retrieval
The result is a simple, real-time localization method using an image retrieval method whose performance is comparable to other monocular camera localization methods which use a map built with LiDARs.
GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps
We approach this task as a sequence tagging problem, where the goal is to produce <POI name, accessibility label> pairs from unstructured text.
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.
Keyphrase Extraction with Span-based Feature Representations
In addition, with the help of tokens, our model is able to extract overlapped keyphrases.
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data.
Knowledge Based Machine Reading Comprehension
Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world.
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Results show that our knowledge-aware model outperforms the state-of-the-art approaches.
Question Generation from SQL Queries Improves Neural Semantic Parsing
We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data.
Learning to Collaborate for Question Answering and Asking
Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model.
Semantic Parsing with Syntax- and Table-Aware SQL Generation
We present a generative model to map natural language questions into SQL queries.
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