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..
A Multi-View Fusion Neural Network for Answer Selection
Authors: Lei Sha, Xiaodong Zhang, Feng Qian, Baobao Chang, Zhifang Sui
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Wiki QA and Sem Eval-2016 CQA datasets demonstrate that our proposed model outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | Lei Sha, Xiaodong Zhang, Feng Qian, Baobao Chang, Zhifang Sui Contributed equally Key Laboratory of Computational Linguistics, Ministry of Education School of Electronics Engineering and Computer Science, Peking University EMAIL |
| Pseudocode | No | The paper describes the model's architecture and calculations using mathematical equations and diagrams, but it does not include a distinct pseudocode block or algorithm. |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We report the performance of our proposed method on two datasets: Wiki QA (Yang, Yih, and Meek 2015) and Sem Eval-2016 CQA (Nakov et al. 2016). |
| Dataset Splits | Yes | Table 2: The statistics of three answer selection datasets. For Wiki QA, we remove all the questions that has no right answers. Dataset (Train / Dev / Test) Wiki QA: 873 / 126 / 243 # of questions, 20360 / 2733 / 6165 # of answers. Sem Eval-2016 CQA: 4879 / 244 / 327 # of questions, 36198 / 2440 / 3270 # of answers. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions "pre-trained Glo Ve" and "Stanford Corenlp" but does not specify version numbers for these or any other software dependencies, which are required for reproducibility. |
| Experiment Setup | Yes | We use 100-dim word embeddings (d = 100) and we set the hidden layer length dh = 500. The external memory length d M is set to 400. The margin is set to 0.1. To compute the network parameter θ, we maximize the max-margin likelihood J(θ) through stochastic gradient descent over shuffled mini-batches with the Adadelta (Zeiler 2012) update rule. |