Conversational Emotion Recognition (CER) has recently been explored through conversational context modeling to learn the emotion distribution, i.e., the likelihood over emotion categories associated with each utterance. While these methods have shown promising results in emotion classification, they often focus on the interactions between utterances (utterance-view) and overlook shifts in the speaker's emotions (emotion-view). This emphasis on homogeneous view modeling limits their overall effectiveness. To address this limitation, we propose DVL-CER, a novel Dual-View Learning approach for CER. DVL-CER integrates both the utterance-view and emotion-view using two projection heads, enabling cross-view projection of emotion distributions. Our approach offers several key advantages: (1) We introduce an emotion-view that captures shifts in a speaker's emotions from initial to subsequent states within a conversation. This view enriches the conversation modeling and supports seamless integration with various CER baseline models. (2) Our dual-view projection learning strategy ﬂexibly balances consistency and independence between the two heterogeneous views, promoting view-specific adaptation learning and incorporating the emotion verification capability within CER. We validate DVL-CER through extensive experiments on two widely-used datasets, IEMOCAP and EmoryNLP. The results demonstrate that DVL-CER achieves state-of-the-art performance, delivering robust and high-quality emotion distributions compared with existing CER methods and other dual-view learning strategies.

Dual-View Learning for Conversational Emotion Recognition through Context and Emotion-Shift Modeling

This paper introduces EmoTransKG, an innovative Emotion Knowledge Graph (EKG) that establishes connections and transformations between emotions across diverse open-textual events. Compared to existing EKGs, which primarily focus on linking emotion keywords to related terms or on assigning sentiment dimension ratings to emotion words by humans, EmoTransKG aims to represent the general knowledge involved in emotion transformation. Specifically, in conversations, successive emotions expressed by a single speaker are temporally considered as the head and tail entities, with open-text utterances (events) occurring between them representing the relation. To explore the knowledge of emotion transformations described in EmoTransKG, we develop a Transformer-based translational model called EmoTransNet, which predictively trains tail entities by interpreting the relation as an operation that transforms the source emotion into the target emotion. Particularly, our designed EmoTransNet serves as a plug-in module that seamlessly integrates with any conversational emotion recognition (CER) models for emotion retrofitting. Experimental results on two CER datasets demonstrate that the incorporation of EmoTransNet with baseline models results in substantial improvements, and the qualitative visualization of entities and relations clearly clarify their unique roles in emotion transformations. These experiments confirm the quality and effectiveness of EmoTransKG.

EmoTransKG: An Innovative Emotion Knowledge Graph to Reveal Emotion Transformation

Generating high-quality responses is a key challenge for any open domain dialogue systems. However, even though there exist a variety of quality dimensions especially designed for dialogue evaluation (e.g., coherence and diversity scores), current dialogue systems rarely utilize them to guide the response generation during training. To alleviate this issue, we propose LSTDial (Long- and Short-Term Dialogue), a novel two-stage framework which generates and utilizes conversation evaluation as explicit feedback during training. Specifically, we fine-tune pre-trained dialogue systems through using turn-level quality feedback in the first stage and further train ever-improving dialogue agents through using dialogue-level quality feedback in the second stage. By using our approach on dialogue systems, capable of enabling dialogue generation with both short-term capabilities (generating more fluent, relevant and varied responses at the turn-level) and long-term capabilities (generating more coherent, engaging and informative responses at the dialogue-level). We implement LSTDial on four strong baseline models and experiment with two open-domain dialogue datasets. Experimental results show that LSTDial achieves significant improvement, enabling to generate better dialogue responses in terms of both human and automatic evaluation.

LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback

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We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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Welcome and hope you all enjoy the conference! 
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The University of Texas at Austin 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Huan Zhao

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Dual-View Learning for Conversational Emotion Recognition through Context and Emotion-Shift Modeling

EmoTransKG: An Innovative Emotion Knowledge Graph to Reveal Emotion Transformation

LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback

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