Name
Abstract | ||
|---|---|---|
ABSTRACTChatty Goose is an open-source Python conversational search framework that provides strong, reproducible reranking pipelines built on recent advances in neural models. The framework comprises extensible modular components that integrate with popular libraries such as Transformers by HuggingFace and ParlAI by Facebook. Our aim is to lower the barrier of entry for research in conversational search by providing reproducible baselines that researchers can build on top of. We provide an overview of the framework and demonstrate how to instantiate a new system from scratch. Chatty Goose incorporates improvements to components that we introduced in the TREC 2019 Conversational Assistance Track (CAsT), where our submission represented the top-performing system. Using our framework, a comparable run can be reproduced with just a few lines of code. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3404835.3462782 | Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Multi-Stage Ranking, Query Reformulation | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edwin Zhang | 1 | 0 | 1.01 |
| Sheng-Chieh Lin | 2 | 23 | 3.88 |
| Jheng-Hong Yang | 3 | 42 | 4.66 |
| Ronak Pradeep | 4 | 13 | 2.44 |
| Rodrigo Nogueira | 5 | 16 | 3.23 |
| Jimmy Lin | 6 | 4800 | 376.93 |