Assisting users in a cross-cultural communication by providing culturally contextualized translations
Bibliographic record
Abstract
In this paper, we present a web-chat application called Culture-to-Chat (C2C). The purpose of this chat is to help users to produce messages in a English as a Second Language - ESL. Regarding this task, C2C has two resources that we named Cultural Translator and Machine Translator. The Cultural Translator uses a Brazilian Portuguese cultural knowledge base (from the Open Mind Common Sense - Br Project in collaboration to Media Lab - MIT) that works with the sender's vocabulary expression in order to provide alternative suggestions that can have the same colloquial meaning. The Machine Translation converts texts from a source language to a target language. The process that we used to combine these features and develop the application was based on an user-centered design approach with a focus on prototyping. We used different types of fidelity-levels (low, mid, high) before developing the functional web prototype version of C2C. User tests were then applied to evaluate usability issues. After collecting data from questionnaires and observation, problems were corrected and now we are heading to a larger user study regarding the C2C functionality. We have been performing a study case involving Brazilian and Canadian users. There are some initial results available from this study that will be discussed further. These data show that users appreciate the resources that help them design messages for cross-cultural communication.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".