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        <title>Flexible retrieval with NMSLIB and FlexNeuART</title>
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            <namePart type="given">Eunjeong</namePart>
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        <name type="personal">
            <namePart type="given">Masato</namePart>
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        <name type="personal">
            <namePart type="given">Dmitrijs</namePart>
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    <abstract>Our objective is to introduce to the NLP community NMSLIB, describe a new retrieval toolkit FlexNeuART, as well as their integration capabilities. NMSLIB, while being one the fastest k-NN search libraries, is quite generic and supports a variety of distance/similarity functions. Because the library relies on the distance-based structure-agnostic algorithms, it can be further extended by adding new distances. FlexNeuART is a modular, extendible and flexible toolkit for candidate generation in IR and QA applications, which supports mixing of classic and neural ranking signals. FlexNeuART can efficiently retrieve mixed dense and sparse representations (with weights learned from training data), which is achieved by extending NMSLIB. In that, other retrieval systems work with purely sparse representations (e.g., Lucene), purely dense representations (e.g., FAISS and Annoy), or only perform mixing at the re-ranking stage.</abstract>
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    <identifier type="doi">10.18653/v1/2020.nlposs-1.6</identifier>
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%0 Conference Proceedings
%T Flexible retrieval with NMSLIB and FlexNeuART
%A Boytsov, Leonid
%A Nyberg, Eric
%Y Park, Eunjeong L.
%Y Hagiwara, Masato
%Y Milajevs, Dmitrijs
%Y Liu, Nelson F.
%Y Chauhan, Geeticka
%Y Tan, Liling
%S Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F boytsov-nyberg-2020-flexible
%X Our objective is to introduce to the NLP community NMSLIB, describe a new retrieval toolkit FlexNeuART, as well as their integration capabilities. NMSLIB, while being one the fastest k-NN search libraries, is quite generic and supports a variety of distance/similarity functions. Because the library relies on the distance-based structure-agnostic algorithms, it can be further extended by adding new distances. FlexNeuART is a modular, extendible and flexible toolkit for candidate generation in IR and QA applications, which supports mixing of classic and neural ranking signals. FlexNeuART can efficiently retrieve mixed dense and sparse representations (with weights learned from training data), which is achieved by extending NMSLIB. In that, other retrieval systems work with purely sparse representations (e.g., Lucene), purely dense representations (e.g., FAISS and Annoy), or only perform mixing at the re-ranking stage.
%R 10.18653/v1/2020.nlposs-1.6
%U https://aclanthology.org/2020.nlposs-1.6
%U https://doi.org/10.18653/v1/2020.nlposs-1.6
%P 32-43

Markdown (Informal)

[Flexible retrieval with NMSLIB and FlexNeuART](https://aclanthology.org/2020.nlposs-1.6) (Boytsov & Nyberg, NLPOSS 2020)

• Flexible retrieval with NMSLIB and FlexNeuART (Boytsov & Nyberg, NLPOSS 2020)

ACL

• Leonid Boytsov and Eric Nyberg. 2020. Flexible retrieval with NMSLIB and FlexNeuART. In Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS), pages 32–43, Online. Association for Computational Linguistics.