<i>XeroPol</i>: Emotion-Aware Contrastive Learning for Zero-Shot Cross-Lingual Politeness Identification in Dialogues
Bibliographic record
Abstract
Politeness is key to successful conversations. It depicts the behavior that is socially valued and is often accompanied by emotions. Previously, researchers have focused on detecting politeness in goal-oriented conversations in high-resource English language. The existing studies do not focus on identifying politeness in a resource-scared Indian languages such as Hindi, primarily due to the lack of labeled data. To overcome this limitation, in this article, we propose a novel emotion-aware contrastive learning (CL) method for zero-shot cross-lingual politeness identification (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XeroPol</i>) task in dialogues. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ContrastiveAligner</i>, a CL-based alignment method for zero-shot cross-lingual transfer. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ContrastiveAligner</i> employs translated data and pushes the model to generate similar utterance embeddings for different languages. As politeness and emotion are interrelated, hence, as the conversation progresses, the variation in emotions tends to pose challenges in identifying politeness in dialogues. Thus, in this work, we also design an auxiliary emotion-aware CL objective using sentiment information, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EmoSenti objective</i>, which is expected to implicitly model the emotion change across utterances and help in the primary task of politeness identification. Experiments on MultiDoGo and EmoWOZ datasets demonstrate that the proposed approach significantly outperforms the baselines. Further analysis such as human evaluation on the EmoInHindi dataset validates the efficacy of the entire approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".