<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="finch-choi-2024-convosense">
    <titleInfo>
        <title>ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sarah</namePart>
        <namePart type="given">E</namePart>
        <namePart type="family">Finch</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jinho</namePart>
        <namePart type="given">D</namePart>
        <namePart type="family">Choi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Mastering commonsense understanding and reasoning is a pivotal skill essential for conducting engaging conversations. While there have been several attempts to create datasets that facilitate commonsense inferences in dialogue contexts, existing datasets tend to lack in-depth details, restate information already present in the conversation, and often fail to capture the multifaceted nature of commonsense reasoning. In response to these limitations, we compile a new synthetic dataset for commonsense reasoning in dialogue contexts using GPT, ℂonvoSense, that boasts greater contextual novelty, offers a higher volume of inferences per example, and substantially enriches the detail conveyed by the inferences. Our dataset contains over 500,000 inferences across 12,000 dialogues with 10 popular inference types, which empowers the training of generative commonsense models for dialogue that are superior in producing plausible inferences with high novelty when compared to models trained on the previous datasets. To the best of our knowledge, ℂonvoSense is the first of its kind to provide such a multitude of novel inferences at such a large scale.</abstract>
    <identifier type="citekey">finch-choi-2024-convosense</identifier>
    <identifier type="doi">10.1162/tacl_a_00659</identifier>
    <location>
        <url>https://aclanthology.org/2024.tacl-1.26</url>
    </location>
    <part>
        <date>2024</date>
        <detail type="volume"><number>12</number></detail>
        <extent unit="page">
            <start>467</start>
            <end>483</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI
%A Finch, Sarah E.
%A Choi, Jinho D.
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F finch-choi-2024-convosense
%X Mastering commonsense understanding and reasoning is a pivotal skill essential for conducting engaging conversations. While there have been several attempts to create datasets that facilitate commonsense inferences in dialogue contexts, existing datasets tend to lack in-depth details, restate information already present in the conversation, and often fail to capture the multifaceted nature of commonsense reasoning. In response to these limitations, we compile a new synthetic dataset for commonsense reasoning in dialogue contexts using GPT, ℂonvoSense, that boasts greater contextual novelty, offers a higher volume of inferences per example, and substantially enriches the detail conveyed by the inferences. Our dataset contains over 500,000 inferences across 12,000 dialogues with 10 popular inference types, which empowers the training of generative commonsense models for dialogue that are superior in producing plausible inferences with high novelty when compared to models trained on the previous datasets. To the best of our knowledge, ℂonvoSense is the first of its kind to provide such a multitude of novel inferences at such a large scale.
%R 10.1162/tacl_a_00659
%U https://aclanthology.org/2024.tacl-1.26
%U https://doi.org/10.1162/tacl_a_00659
%P 467-483

Markdown (Informal)

[ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI](https://aclanthology.org/2024.tacl-1.26) (Finch & Choi, TACL 2024)

• ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI (Finch & Choi, TACL 2024)

ACL

• Sarah E. Finch and Jinho D. Choi. 2024. ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI. Transactions of the Association for Computational Linguistics, 12:467–483.