Machine Translation with Real-Time Web Search

Authors: Lei Cui, Ming Zhou, Qiming Chen, Dongdong Zhang, Mu Li

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimental results show that our web-based machine translation method demonstrates very promising performance in leveraging fresh translation knowledge and making translation decisions.
Researcher Affiliation Collaboration 1Harbin Institute of Technology, Harbin, P.R. China leicui@hit.edu.cn 2Microsoft Research, Beijing, P.R. China {mingzhou,dozhang,muli}@microsoft.com 3Shanghai Jiao Tong University, Shanghai, P.R. China simoncqm@gmail.com
Pseudocode Yes Algorithm 1 Decoding in web-based MT
Open Source Code No The paper does not provide an explicit statement about releasing the code or a link to a code repository.
Open Datasets Yes We evaluated phrase-level translation quality using a public dataset released by (Huang, Zhang, and Vogel 2005) and compared the accuracy to that approach.
Dataset Splits Yes The development data for parameter tuning with MERT (Och 2003) is a human annotated dataset with 1,483 sentences for both the baseline and our web-based MT system.
Hardware Specification No The paper mentions "a distributed system that contained 48 nodes" but lacks specific details such as CPU/GPU models, memory, or clock speeds.
Software Dependencies No No specific software versions for libraries, frameworks, or programming languages were mentioned.
Experiment Setup Yes In our experiments, was set to 3 and was set to 10.