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].
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation
Authors: Jingjing Gong, Xinchi Chen, Tao Gui, Xipeng Qiu6457-6464
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our model obtains significant improvements on eight corpora with heterogeneous segmentation criteria, compared to the previous method and single-criterion learning. Table 3: Results of the proposed model on the test sets of eight CWS datasets. |
| Researcher Affiliation | Academia | Jingjing Gong, Xinchi Chen, Tao Gui, Xipeng Qiu Shanghai Key Laboratory of Intelligent Information Processing, Fudan University School of Computer Science, Fudan University Shanghai Institute of Intelligent Electronics and Systems EMAIL |
| Pseudocode | No | The paper provides architectural diagrams and mathematical formulations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Datasets We experiment on eight CWS datasets from SIGHAN2005 (Emerson 2005) and SIGHAN2008 (Jin and Chen 2008). The three commonly-used corpora, PKU s People s Daily (PKU) (Yu et al. 2001), Penn Chinese Treebank (CTB) (Fei 2000) and MSRA (Emerson 2005), use different segmentation criteria. |
| Dataset Splits | Yes | We randomly pick 10% instances from training set as the development set for all the datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'word2vec toolkit (Mikolov et al. 2013)' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The character embedding size de is set to 100, task embedding size is set to 20, the hidden size dh for our proposed Switch-LSTMs are set to 100, the number of choices K in K-way switch is set to one of {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}. As a common approach to alleviate overfitting, we dropout our embedding with a probability of 0.2. Other than embedding, we use Xavier uniform initializer for all trainable parameters in our model. For each training step, we sample 6 tasks from the task pool, each with a batch size of 128... The training process is terminated after 7 epochs without improvement on development set. |