Rule-based methods for temporal knowledge graph (TKG) forecasting are valued for the interpretability of their explicit logic chains. However, they suffer from a severe cold-start coverage gap: exact-match closed walks terminate at sparse entities, leaving many queries without any rule evidence. Existing similarity-based remedies are unreliable because their similarity is derived from static structural co-occurrence or shallow embeddings with no temporal calibration and hand-tuned thresholds. To address these limitations, we propose SimRule, a collaborative recommendation framework for TKG rule mining. SimRule first mines both exact rules and collaborative-surrogate rules through a temporally calibrated dual-view entity similarity index. An adaptive dual-branch fusion then ranks candidate objects from their aggregated evidence, and each forecast remains fully auditable as an explicit rule chain with quantified time gaps. On ICEWS14, ICEWS18, and ICEWS05-15, SimRule achieves state-of-the-art MRR and Hits@K (K ≥ 3) among rule-based methods.

To understand how training on conversational language impacts performance of pre-trained models on downstream dialogue tasks, we build compact Transformer-based Language Models from scratch on several large corpora of conversational data. We compare the performance and characteristics of these models against BERT and other strong baselines on dialogue probing tasks. Commercial dialogue systems typically require a small footprint and fast execution time, but recent trends are in the other direction, with an ever-increasing number of parameters, resulting in difficulties in model deployment. We focus instead on training fast, lightweight models that excel at natural language understanding (NLU) and can replace existing lower-capacity conversational AI models with similar size and speed. In the process, we develop a simple but unique curriculum-based approach that moves from general-purpose to dialogue-targeted both in terms of data and objective. Our resultant models have around 1/3 the number of parameters of BERT-base and produce better representations for a wide array of intent detection datasets using linear and Mutual-Information probing techniques. Additionally, the models can be easily fine-tuned on a single consumer GPU card and deployed in near real-time production environments.