578 citations · 2.9k across the 69 of their papers we have counts for
28 papers · 1 filter
EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Qingyan Guo, Rui Wang, Junliang Guo +6
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper,…
Extract and Attend: Improving Entity Translation in Neural Machine Translation
Zixin Zeng, Rui Wang, Yichong Leng +4
While Neural Machine Translation(NMT) has achieved great progress in recent years, it still suffers from inaccurate translation of entities (e.g., person/organization name, locatio…
ResiDual: Transformer with Dual Residual Connections
Shufang Xie, Huishuai Zhang, Junliang Guo +6
Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance. However, the optimal way to implement residual connections in…
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
Yongliang Shen, Kaitao Song, Xu Tan +3
Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various do…
N-Gram Nearest Neighbor Machine Translation
Rui Lv, Junliang Guo, Rui Wang +3
Nearest neighbor machine translation augments the Autoregressive Translation~(AT) with -nearest-neighbor retrieval, by comparing the similarity between the token-level context r…
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition
Yichong Leng, Xu Tan, Wenjie Liu +6
Error correction in automatic speech recognition (ASR) aims to correct those incorrect words in sentences generated by ASR models. Since recent ASR models usually have low word err…
A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation
Kexun Zhang, Rui Wang, Xu Tan +4
It is difficult for non-autoregressive translation (NAT) models to capture the multi-modal distribution of target translations due to their conditional independence assumption, whi…
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling
Kaitao Song, Yichong Leng, Xu Tan +3
Sentence scoring aims at measuring the likelihood score of a sentence and is widely used in many natural language processing scenarios, like reranking, which is to select the best…
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition
Yichong Leng, Xu Tan, Rui Wang +8
Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence, and can further reduce the word error rate (WER). Although multiple ca…
Analyzing and Mitigating Interference in Neural Architecture Search
Jin Xu, Xu Tan, Kaitao Song +5
Weight sharing is a popular approach to reduce the cost of neural architecture search (NAS) by reusing the weights of shared operators from previously trained child models. However…
A Survey on Low-Resource Neural Machine Translation
Rui Wang, Xu Tan, Renqian Luo +2
Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has…
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search
Jin Xu, Xu Tan, Renqian Luo +4
While pre-trained language models (e.g., BERT) have achieved impressive results on different natural language processing tasks, they have large numbers of parameters and suffer fro…
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
Yichong Leng, Xu Tan, Linchen Zhu +7
Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER) than original ASR…
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition
Linghui Meng, Jin Xu, Xu Tan +3
In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking…
Task-Level Curriculum Learning for Non-Autoregressive Neural Machine Translation
Jinglin Liu, Yi Ren, Xu Tan +4
Non-autoregressive translation (NAT) achieves faster inference speed but at the cost of worse accuracy compared with autoregressive translation (AT). Since AT and NAT can share mod…
A Study of Non-autoregressive Model for Sequence Generation
Yi Ren, Jinglin Liu, Xu Tan +3
Non-autoregressive (NAR) models generate all the tokens of a sequence in parallel, resulting in faster generation speed compared to their autoregressive (AR) counterparts but at th…
A Study of Multilingual Neural Machine Translation
Xu Tan, Yichong Leng, Jiale Chen +3
Multilingual neural machine translation (NMT) has recently been investigated from different aspects (e.g., pivot translation, zero-shot translation, fine-tuning, or training from s…
Microsoft Research Asia's Systems for WMT19
Yingce Xia, Xu Tan, Fei Tian +11
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place fo…
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Tomoki Hayashi, Ryuichi Yamamoto, Katsuki Inoue +6
This paper introduces a new end-to-end text-to-speech (E2E-TTS) toolkit named ESPnet-TTS, which is an extension of the open-source speech processing toolkit ESPnet. The toolkit sup…
Efficient Bidirectional Neural Machine Translation
Xu Tan, Yingce Xia, Lijun Wu +1
The encoder-decoder based neural machine translation usually generates a target sequence token by token from left to right. Due to error propagation, the tokens in the right side o…
Multilingual Neural Machine Translation with Language Clustering
Xu Tan, Jiale Chen, Di He +3
Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the t…
Hard but Robust, Easy but Sensitive: How Encoder and Decoder Perform in Neural Machine Translation
Tianyu He, Xu Tan, Tao Qin
Neural machine translation (NMT) typically adopts the encoder-decoder framework. A good understanding of the characteristics and functionalities of the encoder and decoder can help…
Language Graph Distillation for Low-Resource Machine Translation
Tianyu He, Jiale Chen, Xu Tan +1
Neural machine translation on low-resource language is challenging due to the lack of bilingual sentence pairs. Previous works usually solve the low-resource translation problem wi…
Unsupervised Pivot Translation for Distant Languages
Yichong Leng, Xu Tan, Tao Qin +2
Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between si…