Weakly Supervised Multi-task Learning for Semantic Parsing
Authors: Bo Shao, Yeyun Gong, Junwei Bao, Jianshu Ji, Guihong Cao, Xiaola Lin, Nan Duan
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a newly constructed dataset and Complex Web Questions show that our proposed method outperforms state-of-the-art methods which demonstrates the effectiveness and robustness of our method. |
| Researcher Affiliation | Collaboration | Bo Shao1,2 , Yeyun Gong2 , Junwei Bao2 , Jianshu Ji3 , Guihong Cao3 , Xiaola Lin1 and Nan Duan2 1School of Data and Computer Science, Sun Yat-sen University 2Microsoft Research Asia 3Microsoft AI and Research, Redmond WA, USA |
| Pseudocode | No | The paper describes its models and methods using mathematical equations and textual descriptions, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 3) We construct a relatively large scale semantic parsing dataset to advance the semantic parsing research. We will release the dataset together with our code1. 1https://github.com/shaoboly/wsmtl |
| Open Datasets | Yes | In this paper we construct a large scale semantic parsing dataset which contains more than 50,000 <question, logical form> pairs over 9 types of questions. |
| Dataset Splits | No | The paper does not explicitly state the training, validation, and test dataset splits with percentages, sample counts, or specific predefined split citations. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Glove word embeddings' and models based on 'GRU-RNN' and 'attention mechanism', but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set the dropout rate to 0.5. The dimension of all hidden vectors and word embedding is 300. Word vocabulary and embedding are not shared between encoder and decoder. In all our experiment, α and β are set to 0.5, λ1 and λ2 are set to 0.1. |