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pro vyhledávání: '"Lu, Xing Han"'
Autor:
Lù, Xing Han
We introduce BM25S, an efficient Python-based implementation of BM25 that only depends on Numpy and Scipy. BM25S achieves up to a 500x speedup compared to the most popular Python-based framework by eagerly computing BM25 scores during indexing and st
Externí odkaz:
http://arxiv.org/abs/2407.03618
We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve real-world tasks in a multi-turn dialogue fashion. To support this problem, we introduce WEBLINX - a large-sc
Externí odkaz:
http://arxiv.org/abs/2402.05930
Retriever-augmented instruction-following models are attractive alternatives to fine-tuned approaches for information-seeking tasks such as question answering (QA). By simply prepending retrieved documents in its input along with an instruction, thes
Externí odkaz:
http://arxiv.org/abs/2307.16877
Code based Language Models (LMs) have shown very promising results in the field of software engineering with applications such as code refinement, code completion and generation. However, the task of time and space complexity classification from code
Externí odkaz:
http://arxiv.org/abs/2305.05379
Publikováno v:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. (2023) 2799-2829
We introduce the StatCan Dialogue Dataset consisting of 19,379 conversation turns between agents working at Statistics Canada and online users looking for published data tables. The conversations stem from genuine intents, are held in English or Fren
Externí odkaz:
http://arxiv.org/abs/2304.01412
Publikováno v:
Findings of the Association for Computational Linguistics: ACL (2022) 926-937
Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment. In this paper, we ask the question: Can we improve QA systems further \emph{post-}deployment based on user interactions? We focu
Externí odkaz:
http://arxiv.org/abs/2204.03025
Publikováno v:
In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pp. 130-135. 2020
One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designe
Externí odkaz:
http://arxiv.org/abs/2012.13978
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Autor:
Lu, Xing Han1 (AUTHOR), Liu, Aihua2 (AUTHOR), Fuh, Shih-Chieh1 (AUTHOR), Lian, Yi3 (AUTHOR), Guo, Liming2 (AUTHOR), Yang, Yi4 (AUTHOR), Marelli, Ariane2 (AUTHOR) ariane.marelli@mcgill.ca, Li, Yue1 (AUTHOR) ariane.marelli@mcgill.ca
Publikováno v:
PLoS ONE. 1/6/2021, Vol. 16 Issue 1, p1-15. 15p.
Akademický článek
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