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..
Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding
Authors: Oren Barkan, Noam Razin, Itzik Malkiel, Ori Katz, Avi Caciularu, Noam Koenigstein3235-3242
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the effectiveness of DSE on five GLUE sentence-pair tasks. DSE significantly outperforms several ELMO variants and other sentence embedding methods, while accelerating computation of the query-candidate sentence-pairs similarities by several orders of magnitude, with an average relative degradation of 4.6% compared to BERT. Furthermore, we show that DSE produces sentence embeddings that reach state-of-the-art performance on universal sentence representation benchmarks. Our code is made publicly available at https://github.com/microsoft/Distilled Sentence-Embedding. |
| Researcher Affiliation | Collaboration | Oren Barkan,*1 Noam Razin,*12 Itzik Malkiel,12 Ori Katz,13 Avi Caciularu,14 Noam Koenigstein12 1Microsoft, 2Tel Aviv University, 3Technion, 4Bar-Ilan University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks, only descriptive text and a schematic illustration (Figure 1). |
| Open Source Code | Yes | Our code is made publicly available at https://github.com/microsoft/Distilled Sentence-Embedding. |
| Open Datasets | Yes | For sentence-pair tasks, our evaluation includes several datasets from the GLUE benchmark: MRPC (Dolan and Brockett, 2005), MNLI (Williams et al., 2018), QQP, QNLI (Wang et al., 2018), and STS-B (Cer et al., 2017). ... Following (Conneau et al. 2017), we opt for pre-training DSE on the All NLI (MNLI + SNLI) dataset. |
| Dataset Splits | Yes | We evaluate DSE on five sentence-pair tasks from the GLUE benchmark (Wang et al. 2018). ... The best model was selected based on the dev set. |
| Hardware Specification | Yes | We conducted two experiments on a single NVIDIA V100 32GB GPU using Py Torch. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We used the Adam optimizer (Kingma and Ba 2014) with minibatch size of 32 and a learning rate of 2e-5, except for STS-B, where we used a learning rate of 1e-5. The models were trained for 8 epochs. |