22 citations · 88 across the 12 of their papers we have counts for
12 papers
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning
Tianxiang Hu, Pei Zhang, Baosong Yang +3
Achieving consistent high-quality machine translation (MT) across diverse domains remains a significant challenge, primarily due to the limited and imbalanced parallel training dat…
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
Ping Guo, Xiangpeng Wei, Yue Hu +4
Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenar…
PolyLM: An Open Source Polyglot Large Language Model
Xiangpeng Wei, Haoran Wei, Huan Lin +15
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been pr…
Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation
Zhiwei Cao, Baosong Yang, Huan Lin +6
-Nearest neighbor machine translation (NN-MT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains. By using an upstre…
Leveraging Large Language Models in Conversational Recommender Systems
Luke Friedman, Sameer Ahuja, David Allen +10
A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Rec…
From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey
Binbin Xie, Jia Song, Liangying Shao +6
Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various…
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
Keqin Bao, Yu Wan, Dayiheng Liu +5
Fine-grained information on translation errors is helpful for the translation evaluation community. Existing approaches can not synchronously consider error position and type, fail…
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
Pei Zhang, Baosong Yang, Haoran Wei +4
Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast…
WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Binbin Xie, Xiangpeng Wei, Baosong Yang +5
Keyphrase generation aims to automatically generate short phrases summarizing an input document. The recently emerged ONE2SET paradigm (Ye et al., 2021) generates keyphrases as a s…
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
Keqin Bao, Yu Wan, Dayiheng Liu +5
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our syst…
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
Yu Wan, Keqin Bao, Dayiheng Liu +5
In this report, we present our submission to the WMT 2022 Metrics Shared Task. We build our system based on the core idea of UNITE (Unified Translation Evaluation), which unifies s…
Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation
Kexin Yang, Dayiheng Liu, Wenqiang Lei +4
Attribute-based Controlled Text Generation (CTG) refers to generating sentences that satisfy desirable attributes (e.g., emotions and topics). Existing works often utilize fine-tun…