Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Machine Translation with Real-Time Web Search
Authors: Lei Cui, Ming Zhou, Qiming Chen, Dongdong Zhang, Mu Li
AAAI 2014 | Venue PDF | 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 EMAIL 2Microsoft Research, Beijing, P.R. China EMAIL 3Shanghai Jiao Tong University, Shanghai, P.R. China EMAIL |
| 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. |