In this paper, we seek to improve the faithfulness of TEMPREL extraction models from two perspectives.The first perspective is to extract genuinely based on contextual description.To achieve this, we propose to conduct counterfactual analysis to attenuate the effects of two significant types of training biases: the event trigger bias and the frequent label bias.We also add tense information into event representations to explicitly place an emphasis on the contextual description.The second perspective is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text.By parameterization of Dirichlet Prior over the model-predicted categorical distribution, we improve the model estimates of the correctness likelihood and make TEMPREL predictions more selective.We also employ temperature scaling to recalibrate the model confidence measure after bias mitigation.Through experimental analysis on MATRES, MATRES-DS, and TDDiscourse, we demonstrate that our model extracts TEMPREL and timelines more faithfully compared to SOTA methods, especially under distribution shifts.

Natural language expresses events with varying granularities, where coarse-grained events (goals) can be broken down into finer-grained event sequences (steps).A critical yet overlooked aspect of understanding event processes is recognizing that not all step events hold equal importance toward the completion of a goal.In this paper, we address this gap by examining the extent to which current models comprehend the essentiality of step events in relation to a goal event.Cognitive studies suggest that such capability enables machines to emulate human commonsense reasoning about preconditions and necessary efforts of everyday tasks.We contribute a high-quality corpus of (goal, step) pairs gathered from the community guideline website WikiHow, with steps manually annotated for their essentiality concerning the goal by experts.The high inter-annotator agreement demonstrates that humans possess a consistent understanding of event essentiality.However, after evaluating multiple statistical and largescale pre-trained language models, we find that existing approaches considerably underperform compared to humans.This observation highlights the need for further exploration into this critical and challenging task 1 .

This paper considers the hybrid precoding design with quantized phase shifters (PSs) for full-duplex millimeter-wave relaying systems, in which the relay node is equipped with PSs of different resolution levels. The joint optimization of relay hybrid precoding and discrete PSs is a high-dimensional non-convex problem with both higher-order polynomial and modulus constraints. To solve this challenging problem, the optimization function under the imperfect channel state information (CSI) is reformulated into a low-complexity norm-minimization form equation. Then, a relaxed algorithm based on the alternating direction method of multipliers and quantization steps is proposed to obtain the coupling approximate solutions for the above optimized equation. And the optimality of analytical solutions is guaranteed based on the imposed termination criterion and necessary conditions. Furthermore, given the requirement of arbitrary quantization, the trade-off between spectral efficiency (SE) and energy efficiency (EE) is investigated under the imperfect CSI. Numerical results verify that the hybrid precoding with low-resolution PSs can achieve excellent SE close to the full-resolution one but with a considerably lighter burden of energy consumption. Moreover, the transmission power plays a positive role in compensating quantization loss, but the excessive value will bring the reduction of EE.