Search Results for author:
Found 23 papers, 12 papers with code
Translating Headers of Tabular Data: A Pilot Study of Schema Translation
Experiments on our dataset demonstrate that CAST significantly outperforms state-of-the-art neural machine translation models.
Benchmarking Meaning Representations in Neural Semantic Parsing
Thus, the impact of meaning representation on semantic parsing is less understood.
Weakly Supervised Semantic Parsing by Learning from Mistakes
Using only 10% of utterance-denotation pairs, the parser achieves 84. 2 denotation accuracy on WikiSQL, which is competitive with the previous state-of-the-art approaches using 100% labeled data.
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges.
Training-Free Semantic Video Composition via Pre-trained Diffusion Model
Therefore, we propose a training-free pipeline employing a pre-trained diffusion model imbued with semantic prior knowledge, which can process composite videos with broader semantic disparities.
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech
Furthermore, experiments on the continuous speech dataset LibriSpeech demonstrate that, by incorporating audio discrimination, CLAD achieves significant performance gain over CL without audio discrimination.
LayoutPrompter: Awaken the Design Ability of Large Language Models
In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning.
A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptions
To model combined and incomplete constraints, we use a Transformer-based layout generation model and carefully design a way to represent constraints and layouts as sequences.
Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery
In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning.
LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models
To tackle the challenge, we summarize three critical factors for achieving a mild forward process for the layout, i. e., legality, coordinate proximity and type disruption.
Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification
Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to learn identity information from labeled images in source domains and apply it to unlabeled images in a target domain.
Domain Adaptive Person Re-Identification
Image Generation
+1
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation
The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications.
LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction
First, to flexibly handle diverse constraints, we propose a constraint serialization scheme, which represents different user constraints as sequences of tokens with a predefined format.
High-Resolution UAV Image Generation for Sorghum Panicle Detection
The number of panicles (or heads) of Sorghum plants is an important phenotypic trait for plant development and grain yield estimation.
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving
We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving.
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing
Recent years pretrained language models (PLMs) hit a success on several downstream tasks, showing their power on modeling language.
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
HiTab provides 10, 686 QA pairs and descriptive sentences with well-annotated quantity and entity alignment on 3, 597 tables with broad coverage of table hierarchies and numerical reasoning types.
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL
However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.
TAPEX: Table Pre-training via Learning a Neural SQL Executor
TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL executor on the diverse, large-scale and high-quality synthetic corpus.
Ranked #1 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context
Recently semantic parsing in context has received considerable attention, which is challenging since there are complex contextual phenomena.
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications
Recent research proposes syntax-based approaches to address the problem of generating programs from natural language specifications.
Robust Confidence Intervals in High-Dimensional Left-Censored Regression
In this paper, we develop smoothed estimating equations that augment the de-biasing method, such that the resulting estimator is adaptive to censoring and is more robust to the misspecification of the error distribution.
