@inproceedings{fu-frank-2024-compositional,
title = "Compositional Structured Explanation Generation with Dynamic Modularized Reasoning",
author = "Fu, Xiyan and
Frank, Anette",
editor = "Bollegala, Danushka and
Shwartz, Vered",
booktitle = "Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.starsem-1.31",
doi = "10.18653/v1/2024.starsem-1.31",
pages = "385--401",
abstract = "In this work, we propose a new task, compositional structured explanation generation (CSEG), to facilitate research on compositional generalization in reasoning. Despite the success of language models in solving reasoning tasks, their compositional generalization capabilities are under-researched. Our new CSEG task tests a model{'}s ability to generalize from generating entailment trees with a limited number of inference steps {--} to more steps, focusing on the length and shapes of entailment trees. CSEG is challenging in requiring both reasoning and compositional generalization abilities, and by being framed as a generation task. Besides the CSEG task, we propose a new dynamic modularized reasoning model, MORSE, that factorizes the inference process into modules, where each module represents a functional unit. We adopt modularized self-attention to dynamically select and route inputs to dedicated heads, which specializes them to specific functions. Using CSEG, we compare MORSE to models from prior work. Our analyses show that the task is challenging, but that the dynamic reasoning modules of MORSE are effective, showing competitive compositional generalization abilities in a generation setting.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="fu-frank-2024-compositional">
<titleInfo>
<title>Compositional Structured Explanation Generation with Dynamic Modularized Reasoning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xiyan</namePart>
<namePart type="family">Fu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anette</namePart>
<namePart type="family">Frank</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Danushka</namePart>
<namePart type="family">Bollegala</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vered</namePart>
<namePart type="family">Shwartz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mexico City, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this work, we propose a new task, compositional structured explanation generation (CSEG), to facilitate research on compositional generalization in reasoning. Despite the success of language models in solving reasoning tasks, their compositional generalization capabilities are under-researched. Our new CSEG task tests a model’s ability to generalize from generating entailment trees with a limited number of inference steps – to more steps, focusing on the length and shapes of entailment trees. CSEG is challenging in requiring both reasoning and compositional generalization abilities, and by being framed as a generation task. Besides the CSEG task, we propose a new dynamic modularized reasoning model, MORSE, that factorizes the inference process into modules, where each module represents a functional unit. We adopt modularized self-attention to dynamically select and route inputs to dedicated heads, which specializes them to specific functions. Using CSEG, we compare MORSE to models from prior work. Our analyses show that the task is challenging, but that the dynamic reasoning modules of MORSE are effective, showing competitive compositional generalization abilities in a generation setting.</abstract>
<identifier type="citekey">fu-frank-2024-compositional</identifier>
<identifier type="doi">10.18653/v1/2024.starsem-1.31</identifier>
<location>
<url>https://aclanthology.org/2024.starsem-1.31</url>
</location>
<part>
<date>2024-06</date>
<extent unit="page">
<start>385</start>
<end>401</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Compositional Structured Explanation Generation with Dynamic Modularized Reasoning
%A Fu, Xiyan
%A Frank, Anette
%Y Bollegala, Danushka
%Y Shwartz, Vered
%S Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F fu-frank-2024-compositional
%X In this work, we propose a new task, compositional structured explanation generation (CSEG), to facilitate research on compositional generalization in reasoning. Despite the success of language models in solving reasoning tasks, their compositional generalization capabilities are under-researched. Our new CSEG task tests a model’s ability to generalize from generating entailment trees with a limited number of inference steps – to more steps, focusing on the length and shapes of entailment trees. CSEG is challenging in requiring both reasoning and compositional generalization abilities, and by being framed as a generation task. Besides the CSEG task, we propose a new dynamic modularized reasoning model, MORSE, that factorizes the inference process into modules, where each module represents a functional unit. We adopt modularized self-attention to dynamically select and route inputs to dedicated heads, which specializes them to specific functions. Using CSEG, we compare MORSE to models from prior work. Our analyses show that the task is challenging, but that the dynamic reasoning modules of MORSE are effective, showing competitive compositional generalization abilities in a generation setting.
%R 10.18653/v1/2024.starsem-1.31
%U https://aclanthology.org/2024.starsem-1.31
%U https://doi.org/10.18653/v1/2024.starsem-1.31
%P 385-401
Markdown (Informal)
[Compositional Structured Explanation Generation with Dynamic Modularized Reasoning](https://aclanthology.org/2024.starsem-1.31) (Fu & Frank, *SEM 2024)
ACL