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Found 46 papers, 8 papers with code
Universal Conditional Masked Language Pre-training for Neural Machine Translation
To the best of our knowledge, this is the first work to pre-train a unified model for fine-tuning on both NMT tasks.
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme
Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composing compatible melodies.
Aligning Large Language Models with Human: A Survey
(2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment.
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models
To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs.
M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models
In this paper, we propose M4LE, a Multi-ability, Multi-range, Multi-task, Multi-domain benchmark for Long-context Evaluation.
Learning to Edit: Aligning LLMs with Knowledge Editing
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models
Large language models (LLMs) are increasingly relied upon for complex multi-turn conversations across diverse real-world applications.
Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption
We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter.
Context-Aware Graph Segmentation for Graph-Based Translation
In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration.
Topic-Informed Neural Machine Translation
In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance.
Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking
We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a novel grounded semantic method for dialogue understanding and task-order management.
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task
This paper describes Huawei{'}s neural machine translation systems for the WMT 2019 biomedical translation shared task.
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation
Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observed, remains challenging due to the syntactic structure difference and simultaneity requirements.
Pretrained Language Models for Document-Level Neural Machine Translation
Previous work on document-level NMT usually focuses on limited contexts because of degraded performance on larger contexts.
Document Graph for Neural Machine Translation
Previous works have shown that contextual information can improve the performance of neural machine translation (NMT).
Future-Guided Incremental Transformer for Simultaneous Translation
Simultaneous translation (ST) starts translations synchronously while reading source sentences, and is used in many online scenarios.
Dependency Graph-to-String Statistical Machine Translation
We present graph-based translation models which translate source graphs into target strings.
An Approach to Improve Robustness of NLP Systems against ASR Errors
Speech-enabled systems typically first convert audio to text through an automatic speech recognition (ASR) model and then feed the text to downstream natural language processing (NLP) modules.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021
We use a unified transformer architecture for our MultiST model, so that the data from different modalities (i. e., speech and text) and different tasks (i. e., Speech Recognition, Machine Translation, and Speech Translation) can be exploited to enhance the model's ability.
Learning Multilingual Representation for Natural Language Understanding with Enhanced Cross-Lingual Supervision
Recently, pre-training multilingual language models has shown great potential in learning multilingual representation, a crucial topic of natural language processing.
RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer
To bridge the modality gap between speech and text, RealTranS gradually downsamples the input speech with interleaved convolution and unidirectional Transformer layers for acoustic modeling, and then maps speech features into text space with a weighted-shrinking operation and a semantic encoder.
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
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training
In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation.
Adversarial Parameter Defense by Multi-Step Risk Minimization
To enhance neural networks, we propose the adversarial parameter defense algorithm that minimizes the average risk of multiple adversarial parameter corruptions.
Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 2021
We use a unified transformer architecture for our MultiST model, so that the data from different modalities (i. e., speech and text) and different tasks (i. e., Speech Recognition, Machine Translation, and Speech Translation) can be exploited to enhance the model’s ability.
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.
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.
Huawei’s Submissions to the WMT20 Biomedical Translation Task
This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task.
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.
NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation
This paper describes the NoahNMT system submitted to the WMT 2021 shared task of Very Low Resource Supervised Machine Translation.
Triangular Transfer: Freezing the Pivot for Triangular Machine Translation
Triangular machine translation is a special case of low-resource machine translation where the language pair of interest has limited parallel data, but both languages have abundant parallel data with a pivot language.
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models
However, labeled cross-lingual corpus is expensive or even inaccessible, especially in the fields where labels are private, such as diagnostic results of symptoms in medicine and user profiles in business.
End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022
This paper describes the system submitted to AutoSimTrans 2022 from Huawei Noah’s Ark Lab, which won the first place in the audio input track of the Chinese-English translation task.
AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation
To alleviate the data scarcity problem in End-to-end speech translation (ST), pre-training on data for speech recognition and machine translation is considered as an important technique.
Evaluating the Efficacy of Length-Controllable Machine Translation
We can use automatic summarization or machine translation evaluation metrics for length-controllable machine translation, but this is not necessarily suitable and accurate.
End-to-end Training and Decoding for Pivot-based Cascaded Translation Model
Utilizing pivot language effectively can significantly improve low-resource machine translation.
