We lifted 6 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| elnaaz/GCE-Model | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| annotate | Ran | elnaaz/GCE-Model/dataset.py pointer only (licence: BSD-3-Clause) · get_code("a8db10c9004d9b8f") |
| missing_files | Ran | elnaaz/GCE-Model/preprocess_data.py pointer only (licence: BSD-3-Clause) · get_code("84d277412226d70d") |
| pad | Ran | elnaaz/GCE-Model/models/gce.py pointer only (licence: BSD-3-Clause) · get_code("a972bbc41289b9a2") |
| position_encoding_init | Ran | elnaaz/GCE-Model/models/gce.py pointer only (licence: BSD-3-Clause) · get_code("e9588d59c2fa46c8") |
| run_rnn | Ran | elnaaz/GCE-Model/models/gce.py pointer only (licence: BSD-3-Clause) · get_code("13617250dc021474") |
| load_model | Not yet run | elnaaz/GCE-Model/utils.py pointer only (licence: BSD-3-Clause) · get_code("a87c666aeb87d9c4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance. This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al. which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. By using only one recurrent networks with global conditioning, compared to (1 + \# slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by $35\%$ on average, while preserving performance of belief state tracking, by $97.38\%$ on turn request and $88.51\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset (Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform and joint goal accuracy.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1812.00899")
get_code_for_paper("1812.00899")
have("1812.00899")
Connect an agent — have() is free.