Search Results for author:
Found 22 papers, 4 papers with code
MT-PATCHER: Selective and Extendable Knowledge Distillation from Large Language Models for Machine Translation
Large Language Models (LLM) have demonstrated their strong ability in the field of machine translation (MT), yet they suffer from high computational cost and latency.
Speech Translation with Large Language Models: An Industrial Practice
Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM.
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation
Document-level Neural Machine Translation (DocNMT) has been proven crucial for handling discourse phenomena by introducing document-level context information.
Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions
Secondly, we find that LLMs' ability to carry out translation instructions relies on the understanding of translation instructions and the alignment among different languages.
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation.
Visual Information Matters for ASR Error Correction
Aiming to improve the Automatic Speech Recognition (ASR) outputs with a post-processing step, ASR error correction (EC) techniques have been widely developed due to their efficiency in using parallel text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation
Therefore, this paper correspondingly establishes new methods and new datasets for MMT.
Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation
The effectiveness of kNNMT directly depends on the quality of retrieved neighbors.
Controlling Styles in Neural Machine Translation with Activation Prompt
Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is crucial for enhancing user experience.
Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts
Domain adaptation is an important challenge for neural machine translation.
Unified Multimodal Punctuation Restoration Framework for Mixed-Modality Corpus
The punctuation restoration task aims to correctly punctuate the output transcriptions of automatic speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Non-Autoregressive Models are Better Multilingual Translators
Multilingual machine translation aims to develop a single model for multiple language directions.
The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
This paper describes the Volctrans' submission to the WMT21 news translation shared task for German->English translation.
Learning Kernel-Smoothed Machine Translation with Retrieved Examples
How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?
Language Tags Matter for Zero-Shot Neural Machine Translation
Language tag (LT) strategies are often adopted to indicate the translation directions in MNMT.
Language-aware Interlingua for Multilingual Neural Machine Translation
However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained.
AR: Auto-Repair the Synthetic Data for Neural Machine Translation
Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translation (BT) or forward translation (FT, or selftraining), into the NMT training process can significantly improve translation quality.
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
The standard paradigm of exploiting them includes two steps: first, pre-training a model, e. g. BERT, with a large scale unlabeled monolingual data.
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages
Our approach is extended from a basic monolingual STS framework to a shared multilingual encoder pretrained with translation task to incorporate rich-resource language data.
Alibaba's Neural Machine Translation Systems for WMT18
We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese.
