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Found 26 papers, 1 papers with code
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.
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.
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 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.
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).
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)
+4
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.
HI-CMLM: Improve CMLM with Hybrid Decoder Input
Mask-predict CMLM (Ghazvininejad et al., 2019) has achieved stunning performance among non-autoregressive NMT models, but we find that the mechanism of predicting all of the target words only depending on the hidden state of [MASK] is not effective and efficient in initial iterations of refinement, resulting in ungrammatical repetitions and slow convergence.
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.
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.
Cross-Domain Audio Deepfake Detection: Dataset and Analysis
Audio deepfake detection (ADD) is essential for preventing the misuse of synthetic voices that may infringe on personal rights and privacy.
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.
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators
In this paper, we propose an alternative adaptation approach, named Decoding-enhanced Multi-phase Prompt Tuning (DeMPT), to make LLMs discriminately model and utilize the inter- and intra-sentence context and more effectively adapt LLMs to context-aware NMT.
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.
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting
The recognition of rare named entities, such as personal names and terminologies, is challenging for automatic speech recognition (ASR) systems, especially when they are not frequently observed in the training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Knowledge-Prompted Estimator: A Novel Approach to Explainable Machine Translation Assessment
Cross-lingual Machine Translation (MT) quality estimation plays a crucial role in evaluating translation performance.
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.
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.
The HW-TSC Video Speech Translation System at IWSLT 2020
The paper presents details of our system in the IWSLT Video Speech Translation evaluation.
