@inproceedings{xu-nie-2026-latent,
title = "From Latent Empathy to Structured Training Signals for Emotional Support Dialogue",
author = "Xu, Linran and
Nie, Xiaomei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1019/",
pages = "23418--23438",
ISBN = "979-8-89176-459-0",
abstract = "Emotional support conversation (ESC) requires responses that recognize users' feelings and situational meaning while balancing relational support with practical orientation. Yet it remains unclear whether improvements from synthetic post-training reflect structured empathy signals or merely stronger rewritten responses. We introduce structured empathic supervision, representing each turn with Feeling/Meaning understanding targets and Subjective/Interpersonal/Objective (S/I/O) response plans. On a coverage-oriented diagnostic suite of 219 turns from 20 ESConv dialogues, Full-SIO-SFT significantly outperforms a decoding- and budget-matched Rewrite-SFT control on aggregate support quality, perceived empathy, counseling quality, and cognitive depth. Controlled perspective analyses show target-aligned specialization: SI favors relational support, whereas IO favors informational support. Starting from Full-SIO-SFT, balanced DPO further improves the aggregate six-dimensional support objective, while directional objectives induce distinct support profiles. Blinded comparative ratings from two psychology-background annotators corroborate the structured-supervision gain and the directional profile shift. These results support structured supervision and preference weighting as complementary mechanisms for controllable ESC post-training."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xu-nie-2026-latent">
<titleInfo>
<title>From Latent Empathy to Structured Training Signals for Emotional Support Dialogue</title>
</titleInfo>
<name type="personal">
<namePart type="given">Linran</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaomei</namePart>
<namePart type="family">Nie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<abstract>Emotional support conversation (ESC) requires responses that recognize users’ feelings and situational meaning while balancing relational support with practical orientation. Yet it remains unclear whether improvements from synthetic post-training reflect structured empathy signals or merely stronger rewritten responses. We introduce structured empathic supervision, representing each turn with Feeling/Meaning understanding targets and Subjective/Interpersonal/Objective (S/I/O) response plans. On a coverage-oriented diagnostic suite of 219 turns from 20 ESConv dialogues, Full-SIO-SFT significantly outperforms a decoding- and budget-matched Rewrite-SFT control on aggregate support quality, perceived empathy, counseling quality, and cognitive depth. Controlled perspective analyses show target-aligned specialization: SI favors relational support, whereas IO favors informational support. Starting from Full-SIO-SFT, balanced DPO further improves the aggregate six-dimensional support objective, while directional objectives induce distinct support profiles. Blinded comparative ratings from two psychology-background annotators corroborate the structured-supervision gain and the directional profile shift. These results support structured supervision and preference weighting as complementary mechanisms for controllable ESC post-training.</abstract>
<identifier type="citekey">xu-nie-2026-latent</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.1019/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>23418</start>
<end>23438</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T From Latent Empathy to Structured Training Signals for Emotional Support Dialogue
%A Xu, Linran
%A Nie, Xiaomei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xu-nie-2026-latent
%X Emotional support conversation (ESC) requires responses that recognize users’ feelings and situational meaning while balancing relational support with practical orientation. Yet it remains unclear whether improvements from synthetic post-training reflect structured empathy signals or merely stronger rewritten responses. We introduce structured empathic supervision, representing each turn with Feeling/Meaning understanding targets and Subjective/Interpersonal/Objective (S/I/O) response plans. On a coverage-oriented diagnostic suite of 219 turns from 20 ESConv dialogues, Full-SIO-SFT significantly outperforms a decoding- and budget-matched Rewrite-SFT control on aggregate support quality, perceived empathy, counseling quality, and cognitive depth. Controlled perspective analyses show target-aligned specialization: SI favors relational support, whereas IO favors informational support. Starting from Full-SIO-SFT, balanced DPO further improves the aggregate six-dimensional support objective, while directional objectives induce distinct support profiles. Blinded comparative ratings from two psychology-background annotators corroborate the structured-supervision gain and the directional profile shift. These results support structured supervision and preference weighting as complementary mechanisms for controllable ESC post-training.
%U https://aclanthology.org/2026.emnlp-main.1019/
%P 23418-23438
Markdown (Informal)
[From Latent Empathy to Structured Training Signals for Emotional Support Dialogue](https://aclanthology.org/2026.emnlp-main.1019/) (Xu & Nie, EMNLP 2026)
ACL