CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Authors: Peixiang Zhong, Di Wang, Pengfei Li, Chen Zhang, Hao Wang, Chunyan Miao14577-14585
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect. |
| Researcher Affiliation | Collaboration | 1 Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University (NTU), Singapore 2 Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, NTU, Singapore 3 School of Electrical and Electronic Engineering, NTU, Singapore 4 Alibaba Group, China |
| Pseudocode | No | The paper describes algorithms and methods but does not present them in a formal "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a repository for the CARE model. |
| Open Datasets | Yes | We conduct experiments on two large-scale datasets, namely Reddit and Twitter. ... we use the emotional tweets (Mohammad 2012; Mohammad et al. 2018) to train the classifier. ... We use Concept Net (Speer, Chin, and Havasi 2017) as our CKG. |
| Dataset Splits | Yes | The statistics of the annotated datasets are presented in Table 3. ... Validation Total 49K (Reddit) / 50K (Twitter) |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or detailed cloud instance names used for running experiments. |
| Software Dependencies | No | The paper mentions using Adam, TransE, Transformer, and GloVe embeddings but does not provide specific version numbers for these or other software libraries/environments (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Our Trans E embeddings have a dimension of 100... Our Transformer model has 1 layer and 4 attention heads... We initialize the word embedding layer with pre-trained Glo Ve embeddings... of size 300. The emotion embedding and feedforward layers have sizes of 50 and 512, respectively. We train our model using Adam... with learning rate of 1, batch size of 64, and dropout of 0.1 for 80K steps, including 6K steps for warmup. We empirically construct 30 relational latent concepts and 10 emotional latent concepts... We use label smoothing of 0.1, total smoothing value of 0.08 for latent concepts in DLS, and top-10 decoding with γ = 1 in CATD. |