8 accepted papers
Understanding multimodal metaphors represents a crucial pathway for machines to comprehend human cognition. However, current research remains constrained by superficial dataset annotations, insufficient systematic evaluation of large language models, and fragmented task frameworks. To bridge these g
Large Language Models (LLMs) exhibit strong reasoning capabilities and are widely applied in event forecasting. However, studies have demonstrated that LLMs exhibit human-like cognitive biases, systematic patterns of deviation from rationality in decision-making. To explore the cognitive biases in e
Although large language models have significantly advanced natural language generation, their potential in low-resource machine translation has not yet been fully explored, especially for languages that translation models have not been trained on. In this study, we provide a detailed demonstration o…
Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical
Complex events generally exhibit unforeseen, multifaceted, and multi-step developments, and cannot be well handled by existing closed-ended event forecasting methods, which are constrained by a limited answer space. In order to accelerate the research on complex event forecasting, we introduce OpenF…
Few-shot slot tagging is an important task in dialogue systems and attracts much attention of researchers. Most previous few-shot slot tagging methods utilize meta-learning procedure for training and strive to construct a large number of different meta tasks to simulate the testing situation of insu…
There are a large number of candidate values shared among slots in multi-domain dialogue state tracking (DST). The existing span prediction-based DST methods generally adopt slot-independent value extraction architecture, which ignore the value sharing. Besides, the slot-independent design leads to…
Prompt-based learning, which exploits knowledge from pre-trained language models by providing textual prompts and designing appropriate answer-category mapping methods, has achieved impressive successes on few-shot text classification and natural language inference (NLI). Because of the diverse ling…