Topic Propagation in Conversational Search
Author:
Affiliation:
1. ISTI-CNR, Pisa, Italy
2. University of Pisa, Pisa, Italy
3. Georgetown University, Georgetown, DC, USA
Funder
Horizon 2020
Italian Ministry of Education and Research
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3397271.3401268
Reference10 articles.
1. M. Aliannejadi M. Chakraborty E.A. Ríssola and F. Crestani. 2020. Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval. In CHIIR. M. Aliannejadi M. Chakraborty E.A. Ríssola and F. Crestani. 2020. Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval. In CHIIR.
2. H. Cao D. Jiang J. Pei Q. He Z. Liao E. Chen and H. Li. 2008. Context-Aware Query Suggestion by Mining Click-through and Session Data. In SIGKDD. ACM. H. Cao D. Jiang J. Pei Q. He Z. Liao E. Chen and H. Li. 2008. Context-Aware Query Suggestion by Mining Click-through and Session Data. In SIGKDD. ACM.
3. M. Gardner J. Grus M. Neumann O. Tafjord P. Dasigi N. F. Liu M. Peters M. Schmitz and L.S. Zettlemoyer. 2017. AllenNLP: A Deep Semantic Natural Language Processing Platform. arXiv:1803.07640 (2017). M. Gardner J. Grus M. Neumann O. Tafjord P. Dasigi N. F. Liu M. Peters M. Schmitz and L.S. Zettlemoyer. 2017. AllenNLP: A Deep Semantic Natural Language Processing Platform. arXiv:1803.07640 (2017).
4. S. Hofstätter N. Rekabsaz C. Eickhoff and A. Hanbury. 2019. On the Effect of Low-Frequency Terms on Neural-IR Models. arXiv:1904.1268 (2019). S. Hofstätter N. Rekabsaz C. Eickhoff and A. Hanbury. 2019. On the Effect of Low-Frequency Terms on Neural-IR Models. arXiv:1904.1268 (2019).
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