Text-attributed graphs (TAGs) underpin many NLP applications, but real-world TAGs are often limited in scale, class balance, and diversity, motivating realistic and controllable TAG generation. Existing generators either replace node text with precomputed embeddings, reducing the task to vector-attributed graph generation, or rely on LLM prompting and agent simulation; neither learns a joint distribution over raw node text, edges, and labels. We propose a controllable substructure generator built from a lightweight causal LM. The model generates connected TAG substructures rather than whole graphs: each training substructure is serialized into lossless token sequences from multiple start nodes. Averaging next-token loss over these traversals treats traversal order as a latent variable and optimizes a variational lower bound on the permutation-invariant substructure marginal likelihood. Training combines continual pretraining for the unconditional distribution with control-prefix fine-tuning that conditions generation on target structural and label descriptors. At inference, a single autoregressive pass per prefix emits a parseable substructure, and the same generator supports distribution-aligned synthesis and boundary-aware augmentation through target-side adaptation alone. Across five real-world TAGs, it produces losslessly parseable graphs, matches or exceeds structure-only generators in structural fidelity, and improves cross-graph GNN generalization.

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.

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.