We lifted 8 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| salesforce/glad | canonical | 4 of 5 |
| kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS | pwc_unofficial | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| attend | Ran | salesforce/glad/models/glad.py code served (permissive licence) · get_code("250b450ed46b01f5") |
| missing_files | Ran | salesforce/glad/preprocess_data.py code served (permissive licence) · get_code("84d277412226d70d") |
| pad | Ran | salesforce/glad/models/glad.py code served (permissive licence) · get_code("a972bbc41289b9a2") |
| run_rnn | Ran | salesforce/glad/models/glad.py code served (permissive licence) · get_code("13617250dc021474") |
| cnet_best_n_paths | Not yet run | kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS/dataset_dstc_clean_asr.py pointer only (licence: BSD-3-Clause) · get_code("b9c0143f90bd1d57") |
| get_cnet_best_pass | Not yet run | kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS/utils.py pointer only (licence: BSD-3-Clause) · get_code("b5fec3b473d9f0d1") |
| load_model | Not yet run | salesforce/glad/utils.py code served (permissive licence) · get_code("a87c666aeb87d9c4") |
| load_model | Not yet run | kolk/MODELING-ASR-AMBIGUITY-FOR-NEURAL-DIALOGUE-STATE-TRACKING-USING-WORD-CONFUSION-NETWORKS/utils.py pointer only (licence: BSD-3-Clause) · get_code("556de295149cb83e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model 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. We show that this significantly improves tracking of rare states and achieves state-of-the-art performance on the WoZ and DSTC2 state tracking tasks. GLAD obtains 88.1% joint goal accuracy and 97.1% request accuracy on WoZ, outperforming prior work by 3.7% and 5.5%. On DSTC2, our model obtains 74.5% joint goal accuracy and 97.5% request accuracy, outperforming prior work by 1.1% and 1.0%.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1805.09655")
get_code_for_paper("1805.09655")
have("1805.09655")
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