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SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats

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arxiv 1909.07734 v2 pith:M43UR4YK submitted 2019-09-17 cs.CL

classification cs.CL
keywords challengedialogueschat-basedchatscontainsdatasetsemotionlinesemotionpush
verification ladder T0 review T1 audit T2 compute T3 formal
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We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting emotions in spoken and chat-based dialogues using augmented EmotionLines datasets. EmotionLines contains two distinct datasets: the first includes excerpts from a US-based TV sitcom episode scripts (Friends) and the second contains online chats (EmotionPush). A total of thirty-six teams registered to participate in the challenge. Eleven of the teams successfully submitted their predictions performance evaluation. The top-scoring team achieved a micro-F1 score of 81.5% for the spoken-based dialogues (Friends) and 79.5% for the chat-based dialogues (EmotionPush).

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Cited by 2 Pith papers

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

  1. Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning

    cs.CL 2025-01 conditional novelty 6.0 of 10

    CMR improves multi-party response generation by contrastively learning speaker styles in a first stage and jointly training response generation with contrastive objectives in a second stage.

  2. Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement

    cs.AI 2026-01 reject novelty 4.0 of 10

    A multi-agent prompt-rewriting loop is claimed to improve LLM emotion diagnosis accuracy, but its evaluation appears to optimize on the test set and lacks replication details.

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