In Japanese–English machine-translation,especially for creative and localized content, standard MT metrics typically summarize translation quality as scalar scores, but provide little diagnostic information about how style changes in translation. This is limiting because politeness, directness, interpersonal stance, and character voice are encoded differently across Japanese and English, and stylistic change may reflect localization-oriented adaptation rather than error. We propose the Style Diagnostic Profile (SDP) framework: SDP assigns each utterance a seven-dimensional bipolar style vector, and 𝛥sdp, the difference between target and source SDP vectors, represents translation-induced stylistic change as a multi-axis shift vector. To evaluate the framework, we first validate an LLM-based SDP scorer against bilingual human annotations on professional JA→EN subtitles and show that it approximates human style judgments. Using Style25, a controlled JA↔EN style-perturbation benchmark, we demonstrate that different perturbation categories occupy distinct regions in 𝛥sdp space. As a case study, we analyze professional subtitles and observe stylistic adaptation rather than simple style copying. These results position the SDP framework as a diagnostic layer complementary to adequacy-oriented MT evaluation.

Over the years, scalar MT metrics have advanced rapidly on benchmarks. Yet they remain black boxes, offering little insight into their decisions and sometimes degrading under out-of-distribution inputs. We introduce Remedy-R, a reasoning-driven generative MT metric trained with reinforcement learning from pairwise translation preferences, without requiring error-span annotations or distillation from closed LLMs. Unlike scalar MT metrics that only outputs translation quality scores, Remedy-R produces step-by-step analyses of accuracy, fluency, and completeness, enabling more interpretable assessments. With only 60K pairwise training samples across two language pairs, Remedy-R remains competitive with top scalar metrics and GPT-4-based judges on WMT22–24 metric benchmarks, generalizes to other languages, and shows strong robustness on OOD stress tests. Moreover, Remedy-R generates self-reflective feedback that can be reused for translation refinement. We validate the faithfulness of such feedback with GPT-4 and show that a simple evaluate–revise pipeline leveraging Remedy-R’s analyses consistently improves translation quality across diverse models without any task-specific tuning.

NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task. Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, thenusing the feedback to update the model. While existing model debugging methods have shown promise, their prototype-level implementations provide limited practical utility. Thus, we propose XMD: the first open-source, end-to-end framework for explanation-based model debugging. Given task- or instance-level explanations,users can flexibly provide various forms of feedback via an intuitive, web-based UI. After receiving user feedback, XMD automatically updates the model in real time, by regularizing the model so that its explanationsalign with the user feedback. The new model can then be easily deployed into real-world applications via Hugging Face. Using XMD, we can improve the model’s OOD performance on text classification tasks by up to 18%.

Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence. Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use. Results on in-domain learning and domain adaptation show that the model’s performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train instances). We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.