b'Zhaopeng Tu'

research

∙ 08/12/2023

GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher

Safety lies at the core of the development of Large Language Models (LLM...

0 Youliang Yuan, et al. ∙

research

∙ 08/07/2023

Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench

Recently, the community has witnessed the advancement of Large Language ...

0 Jen-tse Huang, et al. ∙

research

∙ 07/16/2023

Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling

Modeling discourse – the linguistic phenomena that go beyond individual ...

0 Longyue Wang, et al. ∙

research

∙ 07/06/2023

On the Cultural Gap in Text-to-Image Generation

One challenge in text-to-image (T2I) generation is the inadvertent refle...

0 Bingshuai Liu, et al. ∙

research

∙ 06/15/2023

Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration

Although instruction-tuned large language models (LLMs) have exhibited r...

0 Chenyang Lyu, et al. ∙

research

∙ 05/25/2023

Revisiting Non-Autoregressive Translation at Scale

In real-world systems, scaling has been critical for improving the trans...

0 Zhihao Wang, et al. ∙

research

∙ 05/18/2023

Cross-modality Data Augmentation for End-to-End Sign Language Translation

End-to-end sign language translation (SLT) aims to convert sign language...

0 Jinhui Ye, et al. ∙

research

∙ 05/17/2023

A Survey on Zero Pronoun Translation

Zero pronouns (ZPs) are frequently omitted in pro-drop languages (e.g. C...

0 Longyue Wang, et al. ∙

research

∙ 04/05/2023

ParroT: Translating During Chat Using Large Language Models

Large language models (LLMs) like ChatGPT and GPT-4 have exhibited remar...

0 Wenxiang Jiao, et al. ∙

research

∙ 04/05/2023

Document-Level Machine Translation with Large Language Models

Large language models (LLMs) such as Chat-GPT can produce coherent, cohe...

0 Longyue Wang, et al. ∙

research

∙ 02/16/2023

Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production

Knowledge-aided dialogue response generation aims at augmenting chatbots...

0 Ante Wang, et al. ∙

research

∙ 01/20/2023

Is ChatGPT A Good Translator? A Preliminary Study

This report provides a preliminary evaluation of ChatGPT for machine tra...

0 Wenxiang Jiao, et al. ∙

research

∙ 10/24/2022

Adapters for Enhanced Modeling of Multilingual Knowledge and Text

Large language models appear to learn facts from the large text corpora ...

0 Yifan Hou, et al. ∙

research

∙ 10/13/2022

Scaling Back-Translation with Domain Text Generation for Sign Language Gloss Translation

Sign language gloss translation aims to translate the sign glosses into ...

0 Jinhui Ye, et al. ∙

research

∙ 10/08/2022

ngram-OAXE: Phrase-Based Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation

Recently, a new training oaxe loss has proven effective to ameliorate th...

0 Cunxiao Du, et al. ∙

research

∙ 05/20/2022

Understanding and Mitigating the Uncertainty in Zero-Shot Translation

Zero-shot translation is a promising direction for building a comprehens...

0 Wenxuan Wang, et al. ∙

research

∙ 03/16/2022

Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation

In this paper, we present a substantial step in better understanding the...

0 Wenxuan Wang, et al. ∙

research

∙ 10/05/2021

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation

Pre-training (PT) and back-translation (BT) are two simple and powerful ...

0 Xuebo Liu, et al. ∙

research

∙ 07/17/2021

On the Copying Behaviors of Pre-Training for Neural Machine Translation

Previous studies have shown that initializing neural machine translation...

0 Xuebo Liu, et al. ∙

research

∙ 06/10/2021

Progressive Multi-Granularity Training for Non-Autoregressive Translation

Non-autoregressive translation (NAT) significantly accelerates the infer...

0 Liang Ding, et al. ∙

research

∙ 06/09/2021

Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation

We propose a new training objective named order-agnostic cross entropy (...

0 Cunxiao Du, et al. ∙

research

∙ 06/02/2021

Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation

Self-training has proven effective for improving NMT performance by augm...

0 Wenxiang Jiao, et al. ∙

research

∙ 06/02/2021

Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation

Knowledge distillation (KD) is commonly used to construct synthetic data...

0 Liang Ding, et al. ∙

research

∙ 05/27/2021

TranSmart: A Practical Interactive Machine Translation System

Automatic machine translation is super efficient to produce translations...

7 Guoping Huang, et al. ∙

research

∙ 12/29/2020

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning

Encoder layer fusion (EncoderFusion) is a technique to fuse all the enco...

0 Xuebo Liu, et al. ∙

research

∙ 12/29/2020

Understanding and Improving Lexical Choice in Non-Autoregressive Translation

Knowledge distillation (KD) is essential for training non-autoregressive...

0 Liang Ding, et al. ∙

research

∙ 12/29/2020

Robust Dialogue Utterance Rewriting as Sequence Tagging

The task of dialogue rewriting aims to reconstruct the latest dialogue u...

0 Jie Hao, et al. ∙

research

∙ 11/07/2020

Rethinking the Value of Transformer Components

Transformer becomes the state-of-the-art translation model, while it is ...

0 Wenxuan Wang, et al. ∙

research

∙ 11/02/2020

Context-Aware Cross-Attention for Non-Autoregressive Translation

Non-autoregressive translation (NAT) significantly accelerates the infer...

0 Liang Ding, et al. ∙

research

∙ 10/24/2020

Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation

Non-Autoregressive machine Translation (NAT) models have demonstrated si...

0 Yongchang Hao, et al. ∙

research

∙ 10/06/2020

On the Sub-Layer Functionalities of Transformer Decoder

There have been significant efforts to interpret the encoder of Transfor...

8 Yilin Yang, et al. ∙

research

∙ 10/06/2020

On the Sparsity of Neural Machine Translation Models

Modern neural machine translation (NMT) models employ a large number of ...

0 Yong Wang, et al. ∙

research

∙ 10/06/2020

Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation

Large-scale training datasets lie at the core of the recent success of n...

0 Wenxiang Jiao, et al. ∙

research

∙ 05/03/2020

How Does Selective Mechanism Improve Self-Attention Networks?

Self-attention networks (SANs) with selective mechanism has produced sub...

0 Xinwei Geng, et al. ∙

research

∙ 05/03/2020

On the Inference Calibration of Neural Machine Translation

Confidence calibration, which aims to make model predictions equal to th...

0 Shuo Wang, et al. ∙

research

∙ 04/28/2020

Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models

Machine translation (MT) systems translate text between different langua...

0 Shilin He, et al. ∙

research

∙ 11/22/2019

Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks

The key challenge of multi-domain translation lies in simultaneously enc...

0 Yong Wang, et al. ∙

research

∙ 11/22/2019

Neuron Interaction Based Representation Composition for Neural Machine Translation

Recent NLP studies reveal that substantial linguistic information can be...

0 Jian Li, et al. ∙

research

∙ 11/20/2019

EmpGAN: Multi-resolution Interactive Empathetic Dialogue Generation

Conventional emotional dialogue system focuses on generating emotion-ric...

0 Qintong Li, et al. ∙

research

∙ 09/05/2019

Multi-Granularity Self-Attention for Neural Machine Translation

Current state-of-the-art neural machine translation (NMT) uses a deep mu...

0 Jie Hao, et al. ∙

research

∙ 09/04/2019

Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons

Recent studies have shown that a hybrid of self-attention networks (SANs...

0 Jie Hao, et al. ∙

research

∙ 09/01/2019

Self-Attention with Structural Position Representations

Although self-attention networks (SANs) have advanced the state-of-the-a...

0 Xing Wang, et al. ∙

research

∙ 09/01/2019

One Model to Learn Both: Zero Pronoun Prediction and Translation

Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but sh...

0 Longyue Wang, et al. ∙

research

∙ 09/01/2019

Towards Understanding Neural Machine Translation with Word Importance

Although neural machine translation (NMT) has advanced the state-of-the-...

0 Shilin He, et al. ∙

research

∙ 06/04/2019

Exploiting Sentential Context for Neural Machine Translation

In this work, we present novel approaches to exploit sentential context ...

0 Xing Wang, et al. ∙

research

∙ 06/03/2019

Assessing the Ability of Self-Attention Networks to Learn Word Order

Self-attention networks (SAN) have attracted a lot of interests due to t...

0 Baosong Yang, et al. ∙

research

∙ 04/21/2019

Dynamic Past and Future for Neural Machine Translation

Previous studies have shown that neural machine translation (NMT) models...

0 Zaixiang Zheng, et al. ∙

research

∙ 04/05/2019

Convolutional Self-Attention Networks

Self-attention networks (SANs) have drawn increasing interest due to the...

0 Baosong Yang, et al. ∙

research

∙ 04/05/2019

Information Aggregation for Multi-Head Attention with Routing-by-Agreement

Multi-head attention is appealing for its ability to jointly extract dif...

0 Jian Li, et al. ∙

research

∙ 04/05/2019

Modeling Recurrence for Transformer

Recently, the Transformer model that is based solely on attention mechan...

0 Jie Hao, et al. ∙

Zhaopeng Tu

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