Rui Liu, Björn Schuller, Haizhou Li, Zheng Lian, Haolin Zuo, Xiaofen Xing
We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mc-eiu/mc-eiu | canonical | 3 of 3 |
| MC-EIU/MC-EIU | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| eval | Ran | mc-eiu/mc-eiu/train_baseline.py pointer only (licence: NONE) · get_code("db31f66167bf8705") |
| eval | Ran | mc-eiu/mc-eiu/train_pretrain.py pointer only (licence: NONE) · get_code("e54205631a4a13d9") |
| multiclass_acc | Ran | mc-eiu/mc-eiu/train_pretrain.py pointer only (licence: NONE) · get_code("142e017e37f584c6") |
| get_mask_from_lengths | Not yet run | MC-EIU/MC-EIU/models/networks/ContextEncoder.py pointer only (licence: NONE) · get_code("7ac986d61113150b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Emotion and Intent Joint Understanding in Multimodal Conversation (MC-EIU) aims to decode the semantic information manifested in a multimodal conversational history, while inferring the emotions and intents simultaneously for the current utterance. MC-EIU is enabling technology for many human-computer interfaces. However, there is a lack of available datasets in terms of annotation, modality, language diversity, and accessibility. In this work, we propose an MC-EIU dataset, which features 7 emotion categories, 9 intent categories, 3 modalities, i.e., textual, acoustic, and visual content, and two languages, i.e., English and Mandarin. Furthermore, it is completely open-source for free access. To our knowledge, MC-EIU is the first comprehensive and rich emotion and intent joint understanding dataset for multimodal conversation. Together with the release of the dataset, we also develop an Emotion and Intent Interaction (EI 2 ) network as a reference system by modeling the deep correlation between emotion and intent in the multimodal conversation. With comparative experiments and ablation studies, we demonstrate the effectiveness of the proposed EI 2 method on the MC-EIU dataset. The dataset and codes will be made available at: https://github.com/MC-EIU/MC-EIU.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.02751")
get_code_for_paper("2407.02751")
have("2407.02751")
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