Commonsense Knowledge Aware Conversation Generation with Graph Attention
Authors: Hao Zhou, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, Xiaoyan Zhu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the proposed model can generate more appropriate and informative responses than stateof-the-art baselines. |
| Researcher Affiliation | Collaboration | Hao Zhou1, Tom Young2, Minlie Huang1 , Haizhou Zhao3, Jingfang Xu3 and Xiaoyan Zhu1 1 Conversational AI Group, AI Lab., Dept. of Computer Science, Tsinghua University Beijing National Research Center for Information Science and Technology, China 2 School of Information and Electronics, Beijing Institute of Technology, China 3 Sogou Inc., Beijing, China |
| Pseudocode | No | The paper describes the model using mathematical equations and textual explanations, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/tuxchow/ccm. |
| Open Datasets | Yes | Commonsense Knowledge Base Concept Net4 is used as the commonsense knowledge base. It contains not only world facts such as Paris is the capital of France that are constantly true, but also informal relations between common concepts that are part of daily knowledge such as A dog is a pet . This feature is desirable in our experiments, because the ability to recognize the informal relations between common concepts is necessary in the open-domain conversation setting. For simplicity, we removed triples containing multi-word entities, and 120,850 triples were retained with 21,471 entities and 44 relations. We adopted 10M reddit single-round dialogs from the site5. Since we target at using commonsense knowledge to facilitate language understanding and generation, we filtered the original corpus with the knowledge triples. If a post-response pair can not be connected by any triple (that is, one entity appears in the post and the other in the response), the pair will be removed. The statistics can be seen in Table 1. We randomly sampled 10,000 pairs for validation. To test how commonsense knowledge can help understand common or rare concepts in a post, we constructed four test sets: highfrequency pairs in which each post has all top 25% frequent words, medium-frequency pairs where each post contains at least one word whose frequency is within the range of 25%75%, low-frequency pairs within the range of 75%-100%, and OOV pairs where each post contains out-of-vocabulary words. Each test set has 5,000 pairs randomly sampled from the dataset6. |
| Dataset Splits | Yes | We randomly sampled 10,000 pairs for validation. |
| Hardware Specification | Yes | The models were ran at most 20 epoches, and the training stage of each model took about a week on a Titan X GPU machine. |
| Software Dependencies | No | The paper states, 'Our model was implemented with Tensorflow7.', but does not provide specific version numbers for TensorFlow or other software dependencies. |
| Experiment Setup | Yes | The encoder and decoder have 2-layer GRU structures with 512 hidden cells for each layer and they do not share parameters. The word embedding size is set to 300. The vocabulary size is limited to 30,000. We used Trans E [Bordes et al., 2013] to obtain entity and relation representations. The embedding size of entities and relations is set to 100. We used the Adam optimizer with a mini-batch size of 100. The learning rate is 0.0001. The models were ran at most 20 epoches, and the training stage of each model took about a week on a Titan X GPU machine. |