Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base
Authors: Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on a large-scale dataset [17] for conversation question answering, which consists of 200K dialogs with 1.6M turns over 12.8M entities from Wikidata. Results verify the beneļ¬ts of modeling dialog memory, and show that our semantic parsing-based approach outperforms a memory network based encoder-decoder model by a huge margin. |
| Researcher Affiliation | Collaboration | 1 The School of Data and Computer Science, Sun Yat-sen University. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China 2 Microsoft Research Asia, Beijing, China |
| Pseudocode | No | The paper describes the proposed model and algorithms in text but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about making the source code available or include links to a code repository. |
| Open Datasets | Yes | We conduct the experiment on the CSQA dataset3. The dataset is created based on Wikidata4, including 152K dialogs for training, and 16K/28K dialogs for development/testing. 3https://amritasaha1812.github.io/CSQA/ |
| Dataset Splits | Yes | We conduct the experiment on the CSQA dataset3. The dataset is created based on Wikidata4, including 152K dialogs for training, and 16K/28K dialogs for development/testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions software components and architectures like GRU and RNNs but does not provide specific version numbers for any software dependencies, libraries, or frameworks used. |
| Experiment Setup | Yes | We use a batch size of 32 for the training process and use Adam optimizer. The initial learning rate is set to 0.001. We use beam search at the inference phase. |