Search Results for author:
Found 12 papers, 5 papers with code
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion
Recently, G2P conversion is viewed as a sequence to sequence task and modeled by RNN or CNN based encoder-decoder framework.
Ranked #1 on
Text-To-Speech Synthesis
on CMUDict 0.7b
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning.
Ranked #3 on
Link Prediction
on FB15k
(MR metric)
Data-Anonymous Encoding for Text-to-SQL Generation
On text-to-SQL generation, the input utterance usually contains lots of tokens that are related to column names or cells in the table, called \textit{table-related tokens}.
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning
While pre-training and fine-tuning, e. g., BERT~\citep{devlin2018bert}, GPT-2~\citep{radford2019language}, have achieved great success in language understanding and generation tasks, the pre-trained models are usually too big for online deployment in terms of both memory cost and inference speed, which hinders them from practical online usage.
Relation-Aware Neighborhood Matching Model for Entity Alignment
Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations.
Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems
Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives.
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
To automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers.
Enhancing Job Recommendation through LLM-based Generative Adversarial Networks
However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion.
Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation
Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models.
Large Language Model with Graph Convolution for Recommendation
To adapt text-based LLMs with structured graphs, We use the LLM as an aggregator in graph processing, allowing it to understand graph-based information step by step.
A multi-stage semi-supervised learning for ankle fracture classification on CT images
Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic.
Spiral Generative Network for Image Extrapolation
In this paper, motivated by human natural ability to perceive unseen surroundings imaginatively, we propose a novel Spiral Generative Network, SpiralNet, to perform image extrapolation in a spiral manner, which regards extrapolation as an evolution process growing from an input sub-image along a spiral curve to an expanded full image.
