Search Results for author:
Found 21 papers, 8 papers with code
TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task
This paper describes TenTrans’ submission to WMT21 Multilingual Low-Resource Translation shared task for the Romance language pairs.
TenTrans High-Performance Inference Toolkit for WMT2021 Efficiency Task
The paper describes the TenTrans’s submissions to the WMT 2021 Efficiency Shared Task.
TenTrans Large-Scale Multilingual Machine Translation System for WMT21
This paper describes TenTrans large-scale multilingual machine translation system for WMT 2021.
CSP:Code-Switching Pre-training for Neural Machine Translation
Unlike traditional pre-training method which randomly masks some fragments of the input sentence, the proposed CSP randomly replaces some words in the source sentence with their translation words in the target language.
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models
Reinforcement Learning (RL) is highly dependent on the meticulous design of the reward function.
Recouple Event Field via Probabilistic Bias for Event Extraction
Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs).
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.
Fully Self-Supervised Learning for Semantic Segmentation
To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.
Ranked #16 on
Unsupervised Semantic Segmentation
on COCO-Stuff-27
Semantic Matching from Different Perspectives
In this paper, we pay attention to the issue which is usually overlooked, i. e., \textit{similarity should be determined from different perspectives}.
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation
The graph network that is established with patches as the nodes can maximize the mutual learning of similar objects.
Energy Aligning for Biased Models
Training on class-imbalanced data usually results in biased models that tend to predict samples into the majority classes, which is a common and notorious problem.
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders
To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation
This eases training by highlighting easy samples that the current model has enough competence to learn.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
+3
Code-switching pre-training for neural machine translation
Unlike traditional pre-training method which randomly masks some fragments of the input sentence, the proposed CSP randomly replaces some words in the source sentence with their translation words in the target language.
FastBERT: a Self-distilling BERT with Adaptive Inference Time
Pre-trained language models like BERT have proven to be highly performant.
A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection
Chinese keyword spotting is a challenging task as there is no visual blank for Chinese words.
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry.
K-BERT: Enabling Language Representation with Knowledge Graph
For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge.
UER: An Open-Source Toolkit for Pre-training Models
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks.
Improving Image Captioning with Conditional Generative Adversarial Nets
In this paper, we propose a novel conditional-generative-adversarial-nets-based image captioning framework as an extension of traditional reinforcement-learning (RL)-based encoder-decoder architecture.
