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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Complementary Learning of Word Embeddings
Authors: Yan Song, Shuming Shi
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that our approach can effectively improve the quality of initial embeddings, in terms of intrinsic and extrinsic evaluations. |
| Researcher Affiliation | Industry | Yan Song, Shuming Shi Tencent AI Lab EMAIL |
| Pseudocode | Yes | Algorithm 1: Complementary learning of word embeddings using CB and SG. |
| Open Source Code | No | The paper does not provide explicit information or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We prepare the latest dump of Wikipedia articles3 as the base corpus for training word embeddings, which contains approximately 2 billion word tokens.3https://dumps.wikimedia.org/enwiki/latest/. We use the MEN-3k [Bruni et al., 2012], Simlex-999 [Hill et al., 2015] and WS-353 [Finkelstein et al., 2002] data sets...The extrinsic evaluation is conducted on text classification with four datasets: the 20Newsgroups (20NG)4 for topic classification, ATIS [Hemphill et al., 1990] for intent classification, TREC [Li and Roth, 2002] for question type classification and IMDB [Maas et al., 2011] for sentiment classification.4The bydate version on the web site: http://qwone.com/~jason/20Newsgroups/ |
| Dataset Splits | Yes | All datasets are organized following their standard split. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | All baseline and our embedding models are trained with the same hyper-parameters, i.e., 200 dimensions, 5 as the word frequency cutoff, a windows size of 5 words, 2 4 iterations, using hierarchical softmax as learning strategy. ... discount learning rates γ1 and γ2 are required as input. ... hyper-parameter λ adjusting the contribution of different sub-rewards. |