10 accepted papers
Human-interaction-involved applications underscore the need for Multi-modal Sentiment Analysis (MSA). Although many approaches have been proposed to address the subtle emotions in different modalities, the power of explanations and temporal alignments is still underexplored. Thus, this paper propose
Large language model (LLM)-based visual dialogue (VD) systems have made response generation for image-grounded conversations more correct and coherent. However, user engagement - the extent to which a user is interested, emotionally involved, and willing to continue the conversation - remains a chal
Scientific images often require accurate numerical representations and correct object attributes. However, current faithfulness metrics are primarily tailored toward photorealistic, real-life imagery, rendering them ill-suited for scientific image evaluation. To address this gap, we introduce a nove
Low-resource language understanding is challenging, even for large language models (LLMs). An epitome of this problem is the CompRehensive lIterary chineSe readIng comprehenSion (CRISIS), whose difficulties include limited linguistic data, long input, and insight-required questions. Besides the comp
We revisit knowledge-based visual reasoning (KB-VR) in light of modern advances in multimodal large language models (MLLMs), and make the following contributions: (i) We propose Visual Knowledge Card (VKC) – a novel image that incorporates not only internal visual knowledge (e.g., scene-aware inform…
Currently, large language models (LLMs) based Open domain Natural language planning (LONG) has considerable room for improvement. E.g., non-reusable plans with incomplete intermediate states and missing steps hinder real-world applications. To remedy these flaws, this paper establishes a dataset wit
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 re…
Writing comprehensive commit messages is tedious yet important, because these messages describe changes of code, such as fixing bugs or adding new features. However, most existing methods focus on either only the changed lines or nearest context lines, without considering the effectiveness of select…
Evaluation metrics shine the light on the best models and thus strongly influence the research directions, such as the recently developed dialogue metrics USR, FED, and GRADE. However, most current metrics evaluate the dialogue data as isolated and static because they only focus on a single quality…
Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in Chinese companies with comments are rare; on the other hand, intensive interaction of attention on Transform…