NLP2CT Lab, University of Macau

LLM-generated-Text-Detection

A survey and reflection on the latest research breakthroughs in LLM-generated Text detection, including data, detectors, metrics, current issues and future directions.

Meta-Curriculum

Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation (AAAI 2021)

ConsistTL

Implementation of our paper in EMNLP 2022, focused on the relationship between parent and child in transfer learning for low-resource NMT

norm-nmt

Norm-Based Curriculum Learning for Neural Machine Translation (ACL 2020)

kNN-TL

UniTE

ua-cl-nmt

Uncertainty-Aware Curriculum Learning for Neural Machine Translation (ACL 2020)

Difficulty-Aware-MT-Evaluation

Difficulty-Aware Machine Translation Evaluation (ACL 2021)

TaU

TaU: Test-time Adaptation for Machine Translation Evaluation by Uncertainty Minimization

TempoSum

Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization

Variance-Aware-MT-Test-Sets

Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

HIL-MT

Human-in-the-loop Machine Translation with Large Language Model

SPL_for_NMT

Self-Paced Learning for Neural Machine Translation (EMNLP 2020)

MultimodalGEC

TransGEC

Unsupervised_Dialect_Translation

Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling (AAAI 2020)

CopyingPenalty

On the Copying Behaviors of Pre-Training for Neural Machine Translation (Findings of ACL 2021)

MoNMT

MoNMT: Modularly Leveraging Monolingual and Bilingual Knowledge for Neural Machine Translation

PrettyAlign

Context-Aware-SAN

Improvements for Self-Attention, which is built based on THUMT

Master-Thesis-stuff

MMKG_MMT

Code repository for the paper "Exploiting Multimodal Knowledge Graph for Multimodal Machine Translation"

MV-Attn

awesome-speech-translation

E-Att

Leveraging

Leveraging Local and Global Patterns for Self-Attention Networks (ACL 2019)

MT-Reading-List

A machine translation reading list maintained by Tsinghua Natural Language Processing Group

WRD

This task is to study how well the word order information learned by different neural networks.