In the era of deep learning, modeling for most natural language processing (NLP) tasks has
converged into several mainstream paradigms. For example, we usually adopt the …

To address the challenges of digital intelligence in the digital economy, artificial intelligence-
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …

For machine reading comprehension, the capacity of effectively modeling the linguistic
knowledge from the detail-riddled and lengthy passages and getting ride of the noises is …

Multi-turn conversation understanding is a major challenge for building intelligent dialogue
systems. This work focuses on retrieval-based response matching for multi-turn conversation …

Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous
driving systems in simulation. This work introduces a data-driven method called TrafficGen …

Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks,
eg, part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc …

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich
contextual syntactic and even semantic meanings. This paper makes the first attempt to …

Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing
artificial intelligence (AI) technologies by explaining their execution processes and results. In …

Attention mechanisms have improved the performance of NLP tasks while allowing models
to remain explainable. Self-attention is currently widely used, however interpretability is …

Emotion-cause pair extraction is a recently proposed task that aims at extracting all potential
clause-level pairs of emotion and cause in text. To solve this task, researchers first proposed …