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Found 47 papers, 6 papers with code
HW-TSC’s Participation in the WMT 2020 News Translation Shared Task
We also conduct experiment with similar language augmentation, which lead to positive results, although not used in our submission.
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels due to its subjectivity.
HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task
The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task.
HW-TSC’s Participation in the WAT 2020 Indic Languages Multilingual Task
This paper describes our work in the WAT 2020 Indic Multilingual Translation Task.
The HW-TSC’s Speech to Speech Translation System for IWSLT 2022 Evaluation
The paper presents the HW-TSC’s pipeline and results of Offline Speech to Speech Translation for IWSLT 2022.
HW-TSC’s Participation in the IWSLT 2022 Isometric Spoken Language Translation
This paper presents our submissions to the IWSLT 2022 Isometric Spoken Language Translation task.
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation
For machine translation part, we pretrained three translation models on WMT21 dataset and fine-tuned them on in-domain corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task.
HW-TSC’s Submissions to the WMT21 Biomedical Translation Task
This paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC).
Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation
Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT.
HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task
This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task.
HW-TSC’s Participation in the WMT 2021 News Translation Shared Task
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT 2021 News Translation Shared Task.
HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task
This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task.
HW-TSC’s Participation in the WMT 2021 Large-Scale Multilingual Translation Task
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to the WMT 2021 Large-Scale Multilingual Translation Task.
Automatic Evaluation Metrics for Document-level Translation: Overview, Challenges and Trends
With the rapid development of deep learning technologies, the field of machine translation has witnessed significant progress, especially with the advent of large language models (LLMs) that have greatly propelled the advancement of document-level translation.
Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Video
The core contribution of our method is expanding the capability of the recent pairwise reconstruction model to long-term incremental dynamic reconstruction by an uncertainty-aware dual memory mechanism.
Chain-of-Description: What I can understand, I can put into words
In this paper, we propose a novel strategy defined as Chain-of-Description (CoD) Prompting, tailored for Multi-Modal Large Language Models.
Doc-Guided Sent2Sent++: A Sent2Sent++ Agent with Doc-Guided memory for Document-level Machine Translation
The contributions of this paper include a detailed analysis of current DocMT research, the introduction of the Sent2Sent++ decoding method, the Doc-Guided Memory mechanism, and validation of its effectiveness across languages and domains.
M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models
Given a unique input $X$, we submit $n$ variations of prompts with $X$ to LLMs in batch mode to decode and derive probability distributions.
Context-aware and Style-related Incremental Decoding framework for Discourse-Level Literary Translation
This report outlines our approach for the WMT24 Discourse-Level Literary Translation Task, focusing on the Chinese-English language pair in the Constrained Track.
Exploring the traditional NMT model and Large Language Model for chat translation
This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English$\leftrightarrow$Germany (en-de) bidirection.
Machine Translation Advancements of Low-Resource Indian Languages by Transfer Learning
This was followed by fine-tuning to achieve bidirectional translation between English and Khasi, as well as English and Mizo.
Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain
This article introduces the submission status of the Translation into Low-Resource Languages of Spain task at (WMT 2024) by Huawei Translation Service Center (HW-TSC).
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to machine translation tasks of the 20th China Conference on Machine Translation (CCMT 2024).
Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT24 general machine translation (MT) shared task, where we participate in the English to Chinese (en2zh) language pair.
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation
Recent advancements in integrating speech information into large language models (LLMs) have significantly improved automatic speech recognition (ASR) accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies.
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction
To tackle this problem, in this paper, we propose the first SfM-free 3DGS-based method for surgical scene reconstruction by jointly optimizing the camera poses and scene representation.
An End-to-End Speech Summarization Using Large Language Model
In this paper, we propose an end-to-end SSum model that utilizes Q-Former as a connector for the audio-text modality and employs LLMs to generate text summaries directly from speech features.
Why Not Transform Chat Large Language Models to Non-English?
For the second issue, we propose a method comprising two synergistic components: low-rank adaptation for training to maintain the original LLM parameters, and recovery KD, which utilizes data generated by the chat LLM itself to recover the original knowledge from the frozen parameters.
A Novel Paradigm Boosting Translation Capabilities of Large Language Models
Experimental results conducted using the Llama2 model, particularly on Chinese-Llama2 after monolingual augmentation, demonstrate the improved translation capabilities of LLMs.
Ada-Tracker: Soft Tissue Tracking via Inter-Frame and Adaptive-Template Matching
Soft tissue tracking is crucial for computer-assisted interventions.
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction
Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation
Moreover, these strategies are more suitable for end-to-end systems than cascade systems.
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion
WLAC predicts a target word given a source sentence, translation context, and a human typed character sequence.
Text Style Transfer Back-Translation
To address this issue, we propose Text Style Transfer Back Translation (TST BT), which uses a style transfer model to modify the source side of BT data.
A Visual Navigation Perspective for Category-Level Object Pose Estimation
In this paper, we take a deeper look at the inference of analysis-by-synthesis from the perspective of visual navigation, and investigate what is a good navigation policy for this specific task.
Diformer: Directional Transformer for Neural Machine Translation
In this paper, we aim to close the gap by preserving the original objective of AR and NAR under a unified framework.
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation
An effective training strategy to improve the performance of AT models is Self-Distillation Mixup (SDM) Training, which pre-trains a model on raw data, generates distilled data by the pre-trained model itself and finally re-trains a model on the combination of raw data and distilled data.
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encoder layers exceeds 18.
Asymptotic in a class of network models with an increasing sub-Gamma degree sequence
For the differential privacy under the sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function.
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
This paper describes our work in participation of the IWSLT-2021 offline speech translation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Priority prediction of Asian Hornet sighting report using machine learning methods
In this paper, we propose a method to predict the priority of sighting reports based on machine learning.
3D Point-to-Keypoint Voting Network for 6D Pose Estimation
In this paper, we propose a framework for 6D pose estimation from RGB-D data based on spatial structure characteristics of 3D keypoints.
PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
Utilizing the trained model under different conditions without data annotation is attractive for robot applications.
SEA: A Combined Model for Heat Demand Prediction
ENN and ARIMA are used to predict seasonal and trend components, respectively.
