We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| emorynlp/chatevaluationplatform | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| custom_needs_gold | Ran | emorynlp/chatevaluationplatform/collection_and_annotations/welcome.py pointer only (licence: NONE) · get_code("b2c725fadbd2a494") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite tremendous advancements in dialogue systems, stable evaluation still requires human judgments producing notoriously high-variance metrics due to their inherent subjectivity. Moreover, methods and labels in dialogue evaluation are not fully standardized, especially for open-domain chats, with a lack of work to compare and assess the validity of those approaches. The use of inconsistent evaluation can misinform the performance of a dialogue system, which becomes a major hurdle to enhance it. Thus, a dimensional evaluation of chat-oriented open-domain dialogue systems that reliably measures several aspects of dialogue capabilities is desired. This paper presents a novel human evaluation method to estimate the rates of many dialogue system behaviors. Our method is used to evaluate four state-of-the-art open-domain dialogue systems and compared with existing approaches. The analysis demonstrates that our behavior method is more suitable than alternative Likert-style or comparative approaches for dimensional evaluation of these systems.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2212.09180")
get_code_for_paper("2212.09180")
have("2212.09180")
Connect an agent — have() is free.