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REVIEW 4 major objections 5 minor 28 references

The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper shows that how finely empathy is defined and how directly labels match that definition predict NLP task transfer better than dataset size or embedding similarity.

desk verdict Valuable empirical framework for empathy task operationalization, but the 'significant' feature-importance claim is not supported by cluster-respecting statistics. read the letter →

arxiv 2501.14981 v1 pith:HQM2GGRD submitted 2025-01-24 cs.CL

Allison Lahnala , Charles Welch , David Jurgens , Lucie Flek This is my paper · ORCID
classification cs.CL
keywords empathyoperationalizationtransferlearningtasktransferabilityconstructvaliditynaturallanguageprocessingadaptertuningtheoreticalgrounding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the practical usefulness of an NLP empathy model is set less by how much data or how similar the datasets are than by how the empathy construct itself is defined and measured. By running 306 intermediate-to-target transfer experiments across 18 empathy tasks from 9 datasets, the authors show that the granularity of a task's empathy definition and how directly its labels correspond to the defined components are the strongest predictors of whether training on one task helps another. Tasks that directly predict specified empathy components transfer better, while tasks built on abstract or holistic empathy ratings rarely improve and often harm a target task. If this is right, building reliable empathy technology means replacing vague, one-number empathy labels with precise multidimensional definitions whose components are directly observable in text.

What carries the argument

The machinery is a three-part construct-grading scheme plus a paired transfer-learning scaffold. Definition rates how fine-grained the cited empathy theory is; Link rates how directly the task labels measure or observe the defined components (e.g., Condolence has a fine-grained appraisal-theory definition but a single abstract empathy rating, earning high Definition and low Link); Conduciveness rates whether the social scenario invites empathic expression. These ratings, averaged across the two annotators, feed an SVR whose permutation importances identify Definition and Link as the predictive features. The transfer scaffold itself, using RoBERTa-base with bottleneck adapters and intermediate adapter training followed by stacked target adapter composition compared against a target-only baseline, generates the 306 paired performance deltas that the ratings are asked to explain.

What would settle it

Have a blinded, independent panel re-rate the 18 tasks with the same rubric and re-run the SVR feature-importance analysis; if the panel's Definition and Link ratings do not reproduce the ordering, or if Definition and Link lose their predictive lead once dataset domain, task format, and label distribution are controlled, the central claim is an artifact of the authors' own ratings.

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Extended reading notes

Core claim

The central claim is that conceptual operationalization, not corpus statistics or representation similarity, governs transferability among empathy tasks. Each of the 18 tasks was rated by two knowledgeable annotators on a 5-point scale for definition granularity, correspondence between the labels and the defined construct, and conduciveness of the language scenario; in a support-vector regression over 306 transfer trials, permutation feature importance for Definition (0.399) and Link (0.292) far exceeded sample size, vocabulary, task-type match, and the sentence-BERT and task-embedding similarities used in prior transfer work (near zero). The qualitative grouping into direct, abstract, and adjacent empathy tasks confirms the pattern: direct tasks as intermediate training most often improve direct and adjacent targets, and abstract targets are never improved. The authors conclude that the granularity of empathy definitions and the directness of measurement-to-construct correspondence are significant factors in transfer performance, and that precise multidimensional operationalizations are necessary.

Load-bearing premise

The entire result rests on the assumption that the two authors' ratings of how fine-grained each task's definition is and how directly its labels match that definition truly capture theoretical grounding, rather than reflecting the authors' expectations or other differences between the tasks.

Editorial extensions

If this is right

  • Fine-grained, multidimensional empathy tasks should be preferred as intermediate training data; abstract single-rating tasks are unlikely to transfer benefits.
  • Abstract empathy targets appear to gain nothing from any of the tested intermediate empathy tasks, so resource construction should avoid purely holistic empathy ratings.
  • Embedding-similarity heuristics (dataset and task embeddings) were near-zero predictors here, so task selection for empathy should use construct-level features rather than relying on those heuristics.
  • Evaluation frameworks for generated empathy in LLMs should measure specific components separately instead of relying on a single empathy score.
  • In limited-data settings, transfer to direct targets showed more harm than help, so transfer should not be assumed to compensate for small training sets.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same three-part rating scheme could be applied to other latent social constructs such as compassion, trust, or emotional support, to test whether definition granularity and label-construct correspondence predict transfer there too.
  • Including non-empathy control tasks in the same 306-pair design would show whether the effects are specific to empathy constructs or a general property of task granularity and label abstraction.
  • The author-rater ratings carry the result; re-annotating the 18 tasks with a blinded external panel and checking that Definition and Link still lead would convert the qualitative categorization into a reproducible instrument.
  • A practical recipe suggested by the paper but not tested is to build new empathy datasets by labeling concrete empathic behaviors or acts, such as question intents, reflections, and expressions of concern, rather than asking annotators for holistic empathy scores.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper studies how the theoretical grounding of empathy operationalizations affects transfer performance across 18 empathy-related NLP tasks. The authors rate each task on Definition granularity, Link between the construct and the measurements, and Conduciveness of the language scenario; they also assign each task to a qualitative theme (direct, abstract, adjacent). They run 306 intermediate-task transfer experiments with adapter-based tuning and fit an SVR to predict transfer improvement from construct features and practical transfer-setting features. They report that Definition and Link are the most predictive features, and that direct empathy tasks transfer better than abstract or adjacent ones. The paper concludes that precise, multidimensional empathy operationalizations and measurements that directly correspond to defined components are needed in NLP.

Significance. If the central claims are supported, this would be a valuable empirical contribution: it would demonstrate that the way empathy is conceptualized has measurable consequences for model transfer, and it would motivate more careful construct development in an area where single abstract ratings are still common. The experimental matrix is substantial, covering 18 tasks and 306 transfer pairs, and the planned release of the trained models will support follow-up work. The paper also proposes an annotation scheme and a qualitative taxonomy that could be reused for other constructs. However, the strength of the current evidence is not commensurate with the abstract's wording: the quantitative claim about 'significant' feature importance lacks a proper null distribution, and the construct ratings come from only two author-annotators. The paper's own Limitations section acknowledges that the qualitative theme grouping is not easily reproducible, yet the abstract states the direct-empathy conclusion without that caveat.

major comments (4)
  1. [§5.1, Table 6] The central claim that Definition and Link are 'significantly more predictive' than other features is not supported by the reported permutation importance. The ± values are standard deviations over ten random train/test splits of the 306 transfer trials, but those trials are not independent: each of the 18 tasks appears as both source and target many times, and its Definition and Link ratings are reused in 17 rows. The effective sample size is therefore much closer to 18 tasks than to 306 pairs, so the reported spread understates the true uncertainty. No null distribution for permutation importance is reported, so the word 'significant' has no inferential backing; moreover, the intervals for Link (0.292 ± 0.091) and Data Source (0.228 ± 0.068) overlap substantially. A cluster-respecting permutation test (e.g., permuting construct ratings across tasks rather than across pairwise rows) is needed before the headline result can be accepted.
  2. [§4.1, Table 2] The top predictive features are the authors' own ratings, which are load-bearing for the paper's main conclusion. With only two annotators, both of whom are authors and know the research hypothesis, high agreement on Definition and Link (Krippendorff's α = 0.86 and 0.83) does not establish that these ratings are unbiased measures of theoretical grounding. The lower agreement on Conduciveness (α = 0.46) shows that at least one rating dimension is not reliably measured. Because the regression's conclusion depends on these variables, the authors should either obtain independent annotations or demonstrate that the feature-importance ranking is robust when the most subjective ratings are excluded or re-rated by external annotators.
  3. [§5.2, Figure 3] The qualitative theme analysis is used to support the abstract's statement that direct empathy tasks have higher transferability, but the counts in Figure 3 are small and no statistical comparison across themes is provided. The Limitations section itself concedes that the theme assignment is an author-deliberated process that is 'not easily reproducible' and that the small differences between groups are not substantial evidence. The abstract and conclusion should be tempered to match this acknowledged limitation, or the authors should provide a formal test of the theme effect that accounts for task-level clustering and multiple comparisons.
  4. [§5.1, experimental design] Because all 18 tasks are empathy-related, the regression cannot distinguish 'theoretical grounding of empathy' from 'general task specificity or annotation granularity' that would predict transfer for any task domain. Without non-empathy control tasks, or at least an explicit discussion of this confound, attributing the predictive power specifically to empathy operationalizations is not fully supported. The authors should either add control tasks or clearly state this limitation in the interpretation of the feature-importance results.
minor comments (5)
  1. [Appendix C, Table 4] The text says models were run for ten epochs, but Table 4 lists epoch counts from 6 to 11; please reconcile this discrepancy.
  2. [§5.1] The SVR implementation and its hyperparameters (kernel, cost, epsilon) are not reported; these details are needed for reproducibility of the permutation importance values.
  3. [Table 1] The column header 'V ocab Size' contains a stray space; please fix the formatting in the table.
  4. [Figure 3] The four panels of Figure 3 are difficult to read because the axis labels are compressed and the panel titles do not clearly indicate which comparison is shown; please enlarge and label all axes consistently.
  5. [References] Several references have inconsistent punctuation and missing venue details (e.g., the WASSA 2023 and EmpatheticDialogues entries); a final copyediting pass would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: transfer performance is measured independently of the construct annotations, and no result reduces by construction to its inputs.

full rationale

The paper's empirical chain is not circular. The construct ratings (Definition, Link, Conduciveness) are human annotations of the tasks' theoretical grounding, while the outcome variable is the measured percent change in target-task performance after intermediate-task adapter training relative to a baseline. These two quantities are operationally distinct: transfer performance comes from independently trained adapters and baselines, not from the ratings or from the fitted regression. The feature-importance analysis permutes each feature in a support vector regression, so finding that Definition and Link are the most predictive features is an empirical association, not an identity. The qualitative themes (direct, abstract, adjacent) were authored through deliberation and correlate with the same ratings, but the transfer counts by theme are computed from the same independent outcome data; this is a grouped re-examination of the evidence, not a new prediction derived from the ratings. The paper's rating criteria cite the authors' prior work (Lahnala et al., 2022), but that citation only motivates the annotation dimensions; it does not supply the transfer results, and the outcome data are external to that citation. No equation in the paper equates Definition or Link to transfer performance by construction, and no fitted parameter is relabeled as a prediction. Statistical concerns about the 'significant' claim (e.g., lack of a cluster-respecting null) are about inferential validity, not circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The ledger is small: no new physical or theoretical entities are introduced. The main hand-chosen inputs are the author ratings and the unspecified SVR hyperparameters, both of which affect the central quantitative claim. The validity of the rating criteria is a domain assumption.

free parameters (2)
  • SVR hyperparameters = not reported
    Support vector regression is fit to predict percent improvement; the kernel, C, epsilon, and gamma are not specified, so the feature-importance results are not fully reproducible.
  • Construct ratings (Definition, Link, Conduciveness) = Table 2 values
    Hand-assigned 5-point Likert scores for 18 tasks by two author-annotators. These are a key input to the regression and are subjective, especially Conduciveness (alpha = 0.46).
assumptions (4)
  • domain assumption Transfer performance is a valid proxy for construct similarity and operationalization quality
    Stated as the paper's hypothesis in Section 1: 'models trained on corpora with more similar constructs should provide more benefit to the target tasks.'
  • domain assumption RoBERTa-base with bottleneck adapters is a sufficient model family for measuring transferability
    Section 5 states adapters 'have often matched the performance of full fine-tuning' and uses only this architecture; the result may not generalize to other models or full fine-tuning.
  • domain assumption The annotation criteria in Figures 4-5 validly operationalize theoretical grounding
    Section 4.1 defines the three aspects; the authors create the criteria and then use them to rate tasks, so validity is assumed rather than externally validated.
  • domain assumption The 18 tasks are representative of empathy NLP research
    Section 3 selects 'widely used datasets', but the set excludes generation tasks and non-English resources, as acknowledged in Limitations.

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Cite this review

Pith. "Pith review of The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs." pith.science (2026). https://pith.science/paper/HQM2GGRD

@misc{pith2026250114981,
  author       = {Pith},
  title        = {Pith review of: The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HQM2GGRD}},
  note         = {Machine review of arXiv:2501.14981}
}
read the original abstract

Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performance of empathy models adapted to empathy tasks with different theoretical groundings. We study (1) the dimensionality of empathy definitions, (2) the correspondence between the defined dimensions and measured/observed properties, and (3) the conduciveness of the data to represent them, finding they have a significant impact to performance compared to other transfer setting features. Characterizing the theoretical grounding of empathy tasks as direct, abstract, or adjacent further indicates that tasks that directly predict specified empathy components have higher transferability. Our work provides empirical evidence for the need for precise and multidimensional empathy operationalizations.

Figures

Figures reproduced from arXiv: 2501.14981 by the authors.

Figure 1
Figure 1. Experimental setup: How does intermediate [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Feature importances in regression fit to im [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Transfer performance by theme. Top: Sig￾nificant improvement (left) and significant harm (right) counts for full data. Bottom: Insignificant difference or significant improvement (left) and significant harm (right) counts for limited data. 5.2 Qualitative Themes and Performance The themes outlined in §4.2 provide researchers a quick and intuitive way to characterize the theo￾retical grounding of their empathy tasks:… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Instructions for the empathy operationalization annotation task. [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]
Figure 5
Figure 5. Figure 5: Criteria provided to the annotators for scoring each aspect of the empathy construct along a 5-point Likert [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]
Figure 6
Figure 6. Figure 6: Classification task heat map of transfer performance of empathy source adapters on empathy target tasks. [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 8
Figure 8. Figure 8: Counts of differences in ratings by the an￾notators on each construct aspect. For Definition and Link, the annotators most frequently differ by zero or one point. However, Conduciveness has a larger degree of disagreement, and the annotators more frequently differ by o…
Figure 7
Figure 7. Figure 7: Regression task heat map of transfer perfor [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]
Figure 9
Figure 9. Figure 9: Heat map of dataset cosine similarity. Calculated with SBERT embeddings averaged over all dataset [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: Heatmap of the task embedding similarity rankings. These are computed by ranking the cosine similarities [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 25 canonical work pages

  1. [1]

    Definition: How granular (coarse/high level to fine/more detailed) is the empathy construct or theory that grounds the data development or task definition?

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  2. [2]

    Link/Correspondence: To what degree do the measurement and/or observation approaches correspond to the construct’s defined components?

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  3. [3]

    Figure 4: Instructions for the empathy operationalization annotation task

    Conduciveness: How does the language scenario/communication context ∗ influence your expectation of observing the empathy construct in the data, considering aspects such as data collection methods (survey, interview, observation, experiment, etc.), annotation criteria, and social norms of the context? ∗Language scenario refers to properties of the corpus ...

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    agreeing, 5) sympathizing, 6) encouraging, 7) suggesting, 8) consoling, and 9) wishing. While Welivita and Pu (Welivita and Pu, 2020) did not develop the scheme on a specific existing empa- thy construct, their work describes several existing theories from psychology and neuroscience which may inform the scheme, including Zillman’s (2008) social emotion d...

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    Ac- cording to Svikhnushina et al

    Amplify joy, 6) Amplify excitement, 7) Sup- port, 8) De-escalate, 9) Offer relief, 10) Amplify pride, 11) Motivate, and 12) Pass judgement. Ac- cording to Svikhnushina et al. (2022), the coding scheme is informed by prior question classification schemes and reference to the principles of emo- tional regulation (Gross, 2013). Their motivation for fine-grai...

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    rate my therapist

    Supporting cognitive and emotional empathic writing of students. In Proceedings of the 59th An- nual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 4063–4077, Online. Association for Computational Linguistics. Xiaorong Wang, Clara Na, Emma...

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    warm, tender, and compassionate feeling for a suffering entity,

    emphasize autonomy, 7) simple reflection, 8) ad- vise with permissionand 9) open question. The non- adherent behaviors include 10) advise without per- mission, 11) self-disclosure, 12) direct, 13) warn, and 14) confront. MI adherent behaviors relate to empathic behaviors and expectations (Moyers and Miller, 2013; Lord et al., 2015; Pérez-Rosas et al., 201...

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    in place of the feed-forward adapter layers; and LoRA (Hu et al., 2022), which instead uses low-rank decomposition matrices in the attention layers. After 500 trials, we found that bottleneck Instructions for Empathy Operationalization Annotation Task CONTEXT We are investigating how empathy constructs are operationalized for NLP research via measurable o...

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Show all 28 references
  1. [14]

    The empathy concept is neither defined nor described nor is there a referenced source from which a definition is drawn

  2. [15]

    The empathy concept defined or referenced bypasses its multidimensionality, e.g., by focusing on a single aspect without relating it to or discussing other possible aspects, by being abstract, simplified, or vague

  3. [16]

    The empathy concept defined or referenced acknowledges more than one dimension of empathy but leaves these dimensions abstract, e.g., by merely referring to emotional and cognitive empathy

  4. [17]

    The empathy concept has a fairly fine granularity. It comprises more than one dimension of empathy, such as cognitive and emotional empathy, with high-level descriptions of how they emerge in language or are experienced and perceived

  5. [18]

    Link/Correspondence

    The empathy concept has a very fine granularity in the dimensions/factors/aspects hypothe- sized to comprise empathic experiences or interactions by including several concrete dimen- sions of empathic experiences, behaviors, or interactions. Link/Correspondence

  6. [19]

    They may capture aspects of empathy or related phenomena, but not of the defined empathy construct by any direct or indirect approach

    The measurements or observations have a very weak correspondence to the defined construct. They may capture aspects of empathy or related phenomena, but not of the defined empathy construct by any direct or indirect approach

  7. [20]

    They seem intended to reflect the empathy construct, but the connection is unclear, or a high level of abstraction likely interferes with capturing what is intended

    The measurements or observations have aweak correspondence to the defined construct. They seem intended to reflect the empathy construct, but the connection is unclear, or a high level of abstraction likely interferes with capturing what is intended

  8. [21]

    They clearly reflect the defined construct, but there is a fair amount of abstraction; some elements of the construct are missing, or the methods may not be reliable

    The measurements or observations somewhat correspond to the defined construct. They clearly reflect the defined construct, but there is a fair amount of abstraction; some elements of the construct are missing, or the methods may not be reliable

  9. [22]

    There may be some abstraction, but they are systematically derived based on several aspects of the construct with moderately reliable methods

    The measurements or observations correspond fairly well to the defined construct. There may be some abstraction, but they are systematically derived based on several aspects of the construct with moderately reliable methods

  10. [23]

    Conduciveness

    The measurements or observations correspond very well to the defined construct; they directly correspond to the individual facets of the construct, and the methods seem highly appropriate and precise for measuring what is intended. Conduciveness

  11. [24]

    Aspects of the scenario significantly lower my expectations of observing empathy

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    Aspects of the scenario somewhat lower my expectations of observing empathy

  13. [26]

    The properties of the scenario do not affect my expectations of observing empathy

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    Aspects of the scenario somewhat increase my expectations of observing empathy

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    Figure 5: Criteria provided to the annotators for scoring each aspect of the empathy construct along a 5-point Likert scale

    Aspects of the scenario significantly increase my expectations of observing empathy. Figure 5: Criteria provided to the annotators for scoring each aspect of the empathy construct along a 5-point Likert scale. Task Definition Link Conducive A B A B A B Condolence 5 5 2 2 3 4 C...

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    Journal of personality, 55(1)

    Distress and empathy: Two qualitatively dis- tinct vicarious emotions with different motivational consequences. Journal of personality, 55(1). Ryan L. Boyd and David M. Markowitz. 2024. Verbal behavior and the future of social science. Ameri- can Psychologist. Place: US Publis...

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    nature” and “specific effective- ness

    Analyzing the “nature” and “specific effective- ness” of clinical empathy: A theoretical overview and contribution towards a theory-based research agenda. Patient Education and Counseling, 74(3):339–346. Theories in Health Communication Research. Damilola Omitaomu, Shabnam Taf...

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    Journal of Pragmatics, 42(10):2615–2619

    Question–response sequences in conversation across ten languages: An introduction. Journal of Pragmatics, 42(10):2615–2619. Alice F Freed. 1994. The form and function of ques- tions in informal dyadic conversation. Journal of pragmatics, 21(6):621–644. Alison Gopnik and Henry ...

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    Computer Speech & Language, 50

    Annotating and modeling empathy in spoken conversations. Computer Speech & Language, 50. Zaid Alyafeai, Maged Saeed AlShaibani, and Irfan Ahmad. 2020. A survey on transfer learning in natural language processing. ArXiv preprint , abs/2007.04239. Mariette Awad, Rahul Khanna, Ma...

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    In Proceed- ings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Me- dia Analysis, pages 92–104, Online

    W ASSA 2021 shared task: Predicting empathy and emotion in reaction to news stories. In Proceed- ings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Me- dia Analysis, pages 92–104, Online. Association for Computational Linguistics. M...

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