Search Results for author:
Found 15 papers, 7 papers with code
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
UniTE: Unified Translation Evaluation
Translation quality evaluation plays a crucial role in machine translation.
Self-Paced Learning for Neural Machine Translation
Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans.
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
In this report, we present our submission to the WMT 2022 Metrics Shared Task.
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).
Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling
As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation.
Uncertainty-Aware Curriculum Learning for Neural Machine Translation
We propose uncertainty-aware curriculum learning, which is motivated by the intuition that: 1) the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and 2) the end of the decline in model uncertainty indicates the completeness of current training stage.
RoBLEURT Submission for WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation
Recently, Minimum Bayes Risk (MBR) decoding has been proposed to improve the quality for NMT, which seeks for a consensus translation that is closest on average to other candidates from the n-best list.
RoBLEURT Submission for the WMT2021 Metrics Task
After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.
Attention Mechanism with Energy-Friendly Operations
Attention mechanism has become the dominant module in natural language processing models.
Challenges of Neural Machine Translation for Short Texts
After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors.
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
In this paper, we propose Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs.
A Unified Framework for Multimodal, Multi-Part Human Motion Synthesis
The field has made significant progress in synthesizing realistic human motion driven by various modalities.
AvatarGPT: All-in-One Framework for Motion Understanding, Planning, Generation and Beyond
AvatarGPT treats each task as one type of instruction fine-tuned on the shared LLM.
