@inproceedings{penha-hauff-2020-slice,
title = "Slice-Aware Neural Ranking",
author = "Penha, Gustavo and
Hauff, Claudia",
editor = "Dalton, Jeff and
Chuklin, Aleksandr and
Kiseleva, Julia and
Burtsev, Mikhail",
booktitle = "Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.scai-1.1",
doi = "10.18653/v1/2020.scai-1.1",
pages = "1--6",
abstract = "Understanding when and why neural ranking models fail for an IR task via error analysis is an important part of the research cycle. Here we focus on the challenges of (i) identifying categories of difficult instances (a pair of question and response candidates) for which a neural ranker is ineffective and (ii) improving neural ranking for such instances. To address both challenges we resort to slice-based learning for which the goal is to improve effectiveness of neural models for slices (subsets) of data. We address challenge (i) by proposing different slicing functions (SFs) that select slices of the dataset{---}based on prior work we heuristically capture different failures of neural rankers. Then, for challenge (ii) we adapt a neural ranking model to learn slice-aware representations, i.e. the adapted model learns to represent the question and responses differently based on the model{'}s prediction of which slices they belong to. Our experimental results (the source code and data are available at \url{https://github.com/Guzpenha/slice_based_learning}) across three different ranking tasks and four corpora show that slice-based learning improves the effectiveness by an average of 2{\%} over a neural ranker that is not slice-aware.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="penha-hauff-2020-slice">
<titleInfo>
<title>Slice-Aware Neural Ranking</title>
</titleInfo>
<name type="personal">
<namePart type="given">Gustavo</namePart>
<namePart type="family">Penha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claudia</namePart>
<namePart type="family">Hauff</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jeff</namePart>
<namePart type="family">Dalton</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aleksandr</namePart>
<namePart type="family">Chuklin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Julia</namePart>
<namePart type="family">Kiseleva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mikhail</namePart>
<namePart type="family">Burtsev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Understanding when and why neural ranking models fail for an IR task via error analysis is an important part of the research cycle. Here we focus on the challenges of (i) identifying categories of difficult instances (a pair of question and response candidates) for which a neural ranker is ineffective and (ii) improving neural ranking for such instances. To address both challenges we resort to slice-based learning for which the goal is to improve effectiveness of neural models for slices (subsets) of data. We address challenge (i) by proposing different slicing functions (SFs) that select slices of the dataset—based on prior work we heuristically capture different failures of neural rankers. Then, for challenge (ii) we adapt a neural ranking model to learn slice-aware representations, i.e. the adapted model learns to represent the question and responses differently based on the model’s prediction of which slices they belong to. Our experimental results (the source code and data are available at https://github.com/Guzpenha/slice_based_learning) across three different ranking tasks and four corpora show that slice-based learning improves the effectiveness by an average of 2% over a neural ranker that is not slice-aware.</abstract>
<identifier type="citekey">penha-hauff-2020-slice</identifier>
<identifier type="doi">10.18653/v1/2020.scai-1.1</identifier>
<location>
<url>https://aclanthology.org/2020.scai-1.1</url>
</location>
<part>
<date>2020-11</date>
<extent unit="page">
<start>1</start>
<end>6</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Slice-Aware Neural Ranking
%A Penha, Gustavo
%A Hauff, Claudia
%Y Dalton, Jeff
%Y Chuklin, Aleksandr
%Y Kiseleva, Julia
%Y Burtsev, Mikhail
%S Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F penha-hauff-2020-slice
%X Understanding when and why neural ranking models fail for an IR task via error analysis is an important part of the research cycle. Here we focus on the challenges of (i) identifying categories of difficult instances (a pair of question and response candidates) for which a neural ranker is ineffective and (ii) improving neural ranking for such instances. To address both challenges we resort to slice-based learning for which the goal is to improve effectiveness of neural models for slices (subsets) of data. We address challenge (i) by proposing different slicing functions (SFs) that select slices of the dataset—based on prior work we heuristically capture different failures of neural rankers. Then, for challenge (ii) we adapt a neural ranking model to learn slice-aware representations, i.e. the adapted model learns to represent the question and responses differently based on the model’s prediction of which slices they belong to. Our experimental results (the source code and data are available at https://github.com/Guzpenha/slice_based_learning) across three different ranking tasks and four corpora show that slice-based learning improves the effectiveness by an average of 2% over a neural ranker that is not slice-aware.
%R 10.18653/v1/2020.scai-1.1
%U https://aclanthology.org/2020.scai-1.1
%U https://doi.org/10.18653/v1/2020.scai-1.1
%P 1-6
Markdown (Informal)
[Slice-Aware Neural Ranking](https://aclanthology.org/2020.scai-1.1) (Penha & Hauff, scai 2020)
ACL
- Gustavo Penha and Claudia Hauff. 2020. Slice-Aware Neural Ranking. In Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI), pages 1–6, Online. Association for Computational Linguistics.