An Unsupervised Model With Attention Autoencoders for Question Retrieval
Authors: Minghua Zhang, Yunfang Wu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the open CQA datasets of Sem Eval-2016 and Sem Eval-2017. |
| Researcher Affiliation | Academia | Key Laboratory of Computational Linguistics, Peking University, MOE, China {zhangmh, wuyf}@pku.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access information (specific link, explicit statement, or mention in supplementary materials) to the source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on the open CQA datasets of Sem Eval-2016 and Sem Eval-2017. These datasets contain real data from the community-created Qatar Living Forums. ... We utilize the large amount of unlabeled data released by the organizers, which consists of 189,941 questions and 1,894,456 comments. |
| Dataset Splits | Yes | The labeled dataset is divided into three folders: training, development and test. Table 1 gives the statistics distribution of the dataset. ... Class Train Dev 2016-Test 2017-Test Original 267 50 70 88 Candidates 2669 500 700 880 Perfect Match 235 59 81 24 Relevant 648 155 152 139 Irrelevant 1586 286 467 717 |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'word2vec' and 'Adam algorithm' but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For the attention autoencoders, the dimensionality of word embeddings was set to 200. ... Both the encoder and decoder consist of a stack of 2 identical layers. The dimension of hidden representation was set to 200... We applied the Adam algorithm... using shuffled mini-batches of size 48. The initial learning rate is 0.0004. The autoencoders learn until the performance in the development data stops improving, with patience p = 3... We set α in equation (18) to 0.035, which was tuned via grid search over the following range of values {0.01, 0.015, 0.02, . . . , 0.1}. |