Sarcasm detection in native English and English as a second language speakers.
Bibliographic record
Abstract
Sarcastic speech is ubiquitous in most languages, though understanding sarcasm is highly dependent upon cultural and social contextual factors (Campbell & Katz, Discourse Processes, 2012, 49, 459). It is therefore surprising that little research has examined the ability of nonnative speakers to understand the sarcastic cues of a second language. In the current study, native English speakers and English as a second language (ESL) speakers were tested in each of four different conditions. Three of the conditions presented isolated cues involved in the detection of sarcasm (prosody, written context, and facial expression) and asked participants to identify the emotional intent of the cue (sarcasm or sincerity). The fourth condition combined spoken context, prosody, and facial expressions into each trial and asked the participant to identify sarcasm or sincerity. Participants also indicated their experience with sarcasm through the completion of three questionnaires: Sarcasm Self-Report Scale (Ivanko et al., Journal of Language and Social Psychology, 2004, 23, 244), the Conversational Indirectness Scale (Holtgraves, Journal of Personality and Social Psychology, 1997, 73, 624), and an Exposure to Sarcasm Scale. Results indicated that there were no differences in the ability of the ESL group to understand sarcasm based on facial expression; however, they were less accurate in identifying the sarcastic written context or prosody than the native English speakers. Taken together with the correlations on the questionnaires, findings suggest that experience plays a key role in the ability of ESL speakers to identify sarcastic cues. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".