Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs
| Autor: | Tomohide Shibata, Manabu Okumura, Shogo Fujita |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Information retrieval Computer science 02 engineering and technology 010501 environmental sciences 01 natural sciences Task (project management) Ranking (information retrieval) Set (abstract data type) Ranking Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Question answering 020201 artificial intelligence & image processing Baseline (configuration management) Computation and Language (cs.CL) 0105 earth and related environmental sciences |
| Zdroj: | COLING |
| DOI: | 10.48550/arxiv.2011.09140 |
| Popis: | In community-based question answering (CQA) platforms, it takes time for a user to get useful information from among many answers. Although one solution is an answer ranking method, the user still needs to read through the top-ranked answers carefully. This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers. Our method is based on determinantal point processes (DPPs), and it calculates the answer importance and similarity between answers by using BERT. We built a dataset focusing on a Japanese CQA site, and the experiments on this dataset demonstrated that the proposed method outperformed several baseline methods. Comment: COLING2020, 12 pages |
| Databáze: | OpenAIRE |
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