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Found 35 papers, 17 papers with code
The NiuTrans’s Submission to the IWSLT22 English-to-Chinese Offline Speech Translation Task
This paper describes NiuTrans’s submission to the IWSLT22 English-to-Chinese (En-Zh) offline speech translation task.
The NiuTrans System for the WMT 2021 Efficiency Task
This paper describes the NiuTrans system for the WMT21 translation efficiency task.
The NiuTrans Machine Translation Systems for WMT20
This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks.
Large Language Models are Parallel Multilingual Learners
In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities.
Soft Alignment of Modality Space for End-to-end Speech Translation
End-to-end Speech Translation (ST) aims to convert speech into target text within a unified model.
Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Significant improvements in end-to-end speech translation (ST) have been achieved through the application of multi-task learning.
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete.
PartialFormer: Modeling Part Instead of Whole
The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead.
Ranked #23 on
Machine Translation
on WMT2014 English-German
Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation
By introducing both cross-modal and conversational representations into the decoder, our model retains context over longer sentences without information loss, achieving relative accuracy improvements of 8. 8% and 23% on Mandarin conversation datasets HKUST and MagicData-RAMC, respectively, compared to the standard Conformer model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort.
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e. g.,} BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences.
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79. 15% accuracy on VQAv2 Test-Std, 86. 56% IR@1 and 95. 64% TR@1 on Flickr30K.
Deliberate then Generate: Enhanced Prompting Framework for Text Generation
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts.
Augmenting Large Language Model Translators via Translation Memories
Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models.
TranSFormer: Slow-Fast Transformer for Machine Translation
Learning multiscale Transformer models has been evidenced as a viable approach to augmenting machine translation systems.
EIT: Enhanced Interactive Transformer
In this paper, we propose a novel architecture, the Enhanced Interactive Transformer (EIT), to address the issue of head degradation in self-attention mechanisms.
Learning Multiscale Transformer Models for Sequence Generation
In this work, we define those scales in different linguistic units, including sub-words, words and phrases.
On Vision Features in Multimodal Machine Translation
Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Inspired by this, we design a new architecture, {\it ODE Transformer}, which is analogous to the Runge-Kutta method that is well motivated in ODE.
The NiuTrans Machine Translation Systems for WMT21
This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks.
The NiuTrans System for WNGT 2020 Efficiency Task
This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task.
The NiuTrans System for the WMT21 Efficiency Task
This paper describes the NiuTrans system for the WMT21 translation efficiency task (http://statmt. org/wmt21/efficiency-task. html).
High-resolution chirplet transform: from parameters analysis to parameters combination
The standard chirplet transform (CT) with a chirp-modulated Gaussian window provides a valuable tool for analyzing linear chirp signals.
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation
We show that a residual block of layers in Transformer can be described as a higher-order solution to ODEs.
Learning Light-Weight Translation Models from Deep Transformer
We proposed a novel group-permutation based knowledge distillation approach to compressing the deep Transformer model into a shallow model.
Shallow-to-Deep Training for Neural Machine Translation
We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly.
A multilayer interstitial fluid flow along vascular adventitia
In real-time observations, the calculated velocity of a continuous ISF flow along fibers of a PACT pathway was 3. 6-15. 6 mm/sec.
Weight Distillation: Transferring the Knowledge in Neural Network Parameters
Inspired by this, we investigate methods of model acceleration and compression in another line of research.
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence.
The NiuTrans Machine Translation Systems for WMT19
We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track.
Learning Deep Transformer Models for Machine Translation
Transformer is the state-of-the-art model in recent machine translation evaluations.
The NiuTrans Machine Translation System for WMT18
This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks.
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
However, it still lacks a sound mathematical explanation on why WNNM is more feasible than NNM.
