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08/21/2023
Patch Is Not All You Need
Vision Transformers have achieved great success in computer visions, del...
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07/05/2023
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?
Recent advancements in Large Language Models (LLMs) such as GPT4 have di...
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10/12/2021
LightSeq2: Accelerated Training for Transformer-based Models on GPUs
Transformer-based neural models are used in many AI applications. Traini...
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02/08/2021
In-Order Chart-Based Constituent Parsing
We propose a novel in-order chart-based model for constituent parsing. C...
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10/23/2020
LightSeq: A High Performance Inference Library for Transformers
Transformer, BERT and their variants have achieved great success in natu...
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09/02/2020
SRQA: Synthetic Reader for Factoid Question Answering
The question answering system can answer questions from various fields a...
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04/30/2020
A Span-based Linearization for Constituent Trees
We propose a novel linearization of a constituent tree, together with a ...
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09/03/2018
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension
In this paper, we introduce Adversarial-and-attention Network (A3Net) fo...
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02/09/2018
