Customer-Service

TL;DR for operators Conversation history helps emotion recognition, but this paper shows that having the whole conversation available is not the same as extracting the affective signal that persists across it. Using the same frozen RoBERTa utterance representations, a structured atmosphere prior scores 71.29 versus 67.86 on IEMOCAP and 69.22 versus 63.63 on MELD; it also improves EmoryNLP and DailyDialog. AtmosERC then reuses that prior inside a lightweight classifier, while a prompt-level variant converts it into a textual cue for LLMs. The lightweight model leads reported baselines on three of four datasets, and the LLM cue improves all three tested general-purpose LLMs on both evaluated datasets. ...

TL;DR for operators Before routing a user request, an automated service must first decide whether the request belongs to any capability it supports. A classifier forced to choose among known intents cannot answer that question reliably through low confidence alone. Xu, Kang, and Lü separate rejection from classification.1 Their gate represents each supported intent with several local acceptance regions because valid requests may form distinct semantic groups. It rejects a request when the request falls outside every region, after comparing each distance with that region’s learned acceptance radius. The method reports the highest out-of-scope F1 in all nine comparisons spanning three datasets and three known-intent ratios. ...