Mingyuan Zhou, Huan Wang, Caiming Xiong, Yihao Feng, Shujian Zhang, Shentao Yang, Jianguo Zhang
We lifted 11 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| shentao-yang/fantastic_reward_iclr2023 | canonical | 7 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| binaryTupleList2Tensor | Ran | shentao-yang/fantastic_reward_iclr2023/RewardTorch.py code served (permissive licence) · get_code("c1f35d36da13f065") |
| cuda_ | Ran | shentao-yang/fantastic_reward_iclr2023/BART.py code served (permissive licence) · get_code("b849278a0ba6c25c") |
| get_state | Ran | shentao-yang/fantastic_reward_iclr2023/EstimateBehaviorPolicy.py code served (permissive licence) · get_code("7f76b00aa1bb20bf") |
| get_turn_state | Ran | shentao-yang/fantastic_reward_iclr2023/EstimateBehaviorPolicy.py code served (permissive licence) · get_code("9f2a9615de6e84bd") |
| invert_mask | Ran | shentao-yang/fantastic_reward_iclr2023/BART.py code served (permissive licence) · get_code("c9f6adbecd206460") |
| puntuation_handler | Ran | shentao-yang/fantastic_reward_iclr2023/utils.py code served (permissive licence) · get_code("a9b8c7a10994073f") |
| shift_tokens_right | Ran | shentao-yang/fantastic_reward_iclr2023/BART.py code served (permissive licence) · get_code("e00ccf7847d12b59") |
| clean_time | Not yet run | shentao-yang/fantastic_reward_iclr2023/damd_multiwoz/clean_dataset.py code served (permissive licence) · get_code("4c62f23ce8135bda") |
| get_state_act | Not yet run | shentao-yang/fantastic_reward_iclr2023/EstimateBehaviorPolicy.py code served (permissive licence) · get_code("a3f6d589f208358b") |
| numpy2torch | Not yet run | shentao-yang/fantastic_reward_iclr2023/RewardTorch.py code served (permissive licence) · get_code("54302690d7f170c5") |
| numpyBinaryTuple2torch | Not yet run | shentao-yang/fantastic_reward_iclr2023/RewardTorch.py code served (permissive licence) · get_code("ef7fddf0c4145890") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve userspecific goals. Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied. This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end (E2E) ToD agents. Specifically, we introduce two generalized objectives for reward-function learning, inspired by the classical learning-to-rank literature. Further, we utilize the learned reward function to guide the training of the E2E ToD agent. With the proposed techniques, we achieve competitive results on the E2E response-generation task on the Multiwoz 2.0 dataset. Source code and checkpoints are publicly released at https://github.com/Shentao-YANG/Fantastic Reward ICLR2023.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.10342")
get_code_for_paper("2302.10342")
have("2302.10342")
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