<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-lee-2020-incorporating">
    <titleInfo>
        <title>Incorporating Behavioral Hypotheses for Query Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ruey-Cheng</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chia-Jung</namePart>
        <namePart type="family">Lee</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 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</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>Generative neural networks have been shown effective on query suggestion. Commonly posed as a conditional generation problem, the task aims to leverage earlier inputs from users in a search session to predict queries that they will likely issue at a later time. User inputs come in various forms such as querying and clicking, each of which can imply different semantic signals channeled through the corresponding behavioral patterns. This paper induces these behavioral biases as hypotheses for query generation, where a generic encoder-decoder Transformer framework is presented to aggregate arbitrary hypotheses of choice. Our experimental results show that the proposed approach leads to significant improvements on top-k word error rate and Bert F1 Score compared to a recent BART model.</abstract>
    <identifier type="citekey">chen-lee-2020-incorporating</identifier>
    <identifier type="doi">10.18653/v1/2020.emnlp-main.251</identifier>
    <location>
        <url>https://aclanthology.org/2020.emnlp-main.251</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>3105</start>
            <end>3110</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Incorporating Behavioral Hypotheses for Query Generation
%A Chen, Ruey-Cheng
%A Lee, Chia-Jung
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-lee-2020-incorporating
%X Generative neural networks have been shown effective on query suggestion. Commonly posed as a conditional generation problem, the task aims to leverage earlier inputs from users in a search session to predict queries that they will likely issue at a later time. User inputs come in various forms such as querying and clicking, each of which can imply different semantic signals channeled through the corresponding behavioral patterns. This paper induces these behavioral biases as hypotheses for query generation, where a generic encoder-decoder Transformer framework is presented to aggregate arbitrary hypotheses of choice. Our experimental results show that the proposed approach leads to significant improvements on top-k word error rate and Bert F1 Score compared to a recent BART model.
%R 10.18653/v1/2020.emnlp-main.251
%U https://aclanthology.org/2020.emnlp-main.251
%U https://doi.org/10.18653/v1/2020.emnlp-main.251
%P 3105-3110

Markdown (Informal)

[Incorporating Behavioral Hypotheses for Query Generation](https://aclanthology.org/2020.emnlp-main.251) (Chen & Lee, EMNLP 2020)

• Incorporating Behavioral Hypotheses for Query Generation (Chen & Lee, EMNLP 2020)

ACL

• Ruey-Cheng Chen and Chia-Jung Lee. 2020. Incorporating Behavioral Hypotheses for Query Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3105–3110, Online. Association for Computational Linguistics.