Pith. sign in

REVIEW 4 cited by

EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2108.12009 v1 pith:2Y7PY4NL submitted 2021-08-26 cs.CL

Taewoon Kim , Piek Vossen This is my paper · ORCID
classification cs.CL
keywords emotionconversationemobertarecognitionspeakerrobertaspeaker-awareutterances
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa, a simple yet expressive scheme of solving the ERC (emotion recognition in conversation) task. By simply prepending speaker names to utterances and inserting separation tokens between the utterances in a dialogue, EmoBERTa can learn intra- and inter- speaker states and context to predict the emotion of a current speaker, in an end-to-end manner. Our experiments show that we reach a new state of the art on the two popular ERC datasets using a basic and straight-forward approach. We've open sourced our code and models at https://github.com/tae898/erc.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations

    cs.CL 2026-05 conditional novelty 6.0 of 10

    SCoPE adds a speaker-conditioned GRU prior gated by predicted emotion shifts to multimodal ERC, reporting state-of-the-art IEMOCAP results and consistent baseline gains.

  2. Towards Designing Social Interventions For Online Climate Change Denialism Discussions

    cs.HC 2025-07 conditional novelty 6.0 of 10

    In-field Reddit bot interventions using insider climate-denial language and linked evidence produced more positive engagement from climate change deniers and additional evidence from supporters.

  3. AtmosERC: Modeling Dialogue-Level Affective Atmosphere for Emotion Recognition in Conversation

    cs.CL 2026-07 conditional novelty 5.0 of 10

    A relation-aware conversational graph can extract a reusable affective-atmosphere prior that modestly improves lightweight and LLM-based emotion recognition in conversation.

  4. EDTalk++: Full Disentanglement for Controllable Talking Head Synthesis

    cs.CV 2025-08 conditional novelty 5.0 of 10

    EDTalk++ disentangles talking-head video into four orthogonal motion banks (mouth, pose, eyes, expression) and drives them from either video or audio inputs.

Pith tools