ASSIST: Towards Label Noise-Robust Dialogue State Tracking

The MultiWOZ 2.0 dataset has greatly boosted the research on dialogue state tracking (DST). However, substantial noise has been discovered in its state annotations. Such noise brings about huge challenges for training DST models robustly. Although several refined versions, including MultiWOZ 2.1-2.4, have been published recently, there are still lots of noisy labels, especially in the training set. Besides, it is costly to rectify all the problematic annotations. In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels. ASSIST first generates pseudo labels for each sample in the training set by using an auxiliary model trained on a small clean dataset, then puts the generated pseudo labels and vanilla noisy labels together to train the primary model. We show the validity of ASSIST theoretically. Experimental results also demonstrate that ASSIST improves the joint goal accuracy of DST by up to $28.16\%$ on MultiWOZ 2.0 and $8.41\%$ on MultiWOZ 2.4, compared to using only the vanilla noisy labels

MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation

The MultiWOZ 2.0 dataset has greatly stimulated the research of task-oriented dialogue systems. However, its state annotations contain substantial noise, which hinders a proper evaluation of model performance. To address this issue, massive efforts were devoted to correcting the annotations. Three improved versions (i.e., MultiWOZ 2.1-2.3) have then been released. Nonetheless, there are still plenty of incorrect and inconsistent annotations. This work introduces MultiWOZ 2.4, which refines the annotations in the validation set and test set of MultiWOZ 2.1. The annotations in the training set remain unchanged (same as MultiWOZ 2.1) to elicit robust and noise-resilient model training. We benchmark eight state-of-the-art dialogue state tracking models on MultiWOZ 2.4. All of them demonstrate much higher performance than on MultiWOZ 2.1

Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting

Query rewriting plays a vital role in enhancing conversational search by transforming context-dependent user queries into standalone forms. Existing approaches primarily leverage human-rewritten queries as labels to train query rewriting models. However, human rewrites may lack sufficient information for optimal retrieval performance. To overcome this limitation, we propose utilizing large language models (LLMs) as query rewriters, enabling the generation of informative query rewrites through well-designed instructions. We define four essential properties for well-formed rewrites and incorporate all of them into the instruction. In addition, we introduce the role of rewrite editors for LLMs when initial query rewrites are available, forming a "rewrite-then-edit" process. Furthermore, we propose distilling the rewriting capabilities of LLMs into smaller models to reduce rewriting latency. Our experimental evaluation on the QReCC dataset demonstrates that informative query rewrites can yield substantially improved retrieval performance compared to human rewrites, especially with sparse retrievers.Comment: 22 pages, accepted to EMNLP Findings 202

Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

Dialogue State Tracking (DST) aims to keep track of users’ intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains. To address these issues, we propose a novel Dynamic Schema Graph Fusion Network (DSGFNet), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations. It also uses the schemata to facilitate knowledge transfer to new domains. DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder. Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1, and MultiWOZ2.2), show that DSGFNet outperforms existing methods

MetaASSIST: Robust Dialogue State Tracking with Meta Learning

Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general framework named ASSIST has recently been proposed to train robust dialogue state tracking (DST) models. It introduces an auxiliary model to generate pseudo labels for the noisy training set. These pseudo labels are combined with vanilla labels by a common fixed weighting parameter to train the primary DST model. Notwithstanding the improvements of ASSIST on DST, tuning the weighting parameter is challenging. Moreover, a single parameter shared by all slots and all instances may be suboptimal. To overcome these limitations, we propose a meta learning-based framework MetaASSIST to adaptively learn the weighting parameter. Specifically, we propose three schemes with varying degrees of flexibility, ranging from slot-wise to both slot-wise and instance-wise, to convert the weighting parameter into learnable functions. These functions are trained in a meta-learning manner by taking the validation set as meta data. Experimental results demonstrate that all three schemes can achieve competitive performance. Most impressively, we achieve a state-of-the-art joint goal accuracy of 80.10% on MultiWOZ 2.4

RNA helicase A interacts with divergent lymphotropic retroviruses and promotes translation of human T-cell leukemia virus type 1

The 5′ untranslated region (UTR) of retroviruses contain structured replication motifs that impose barriers to efficient ribosome scanning. Two RNA structural motifs that facilitate efficient translation initiation despite a complex 5′ UTR are internal ribosome entry site (IRES) and 5′ proximal post-transcriptional control element (PCE). Here, stringent RNA and protein analyses determined the 5′ UTR of spleen necrosis virus (SNV), reticuloendotheliosis virus A (REV-A) and human T-cell leukemia virus type 1 (HTLV-1) exhibit PCE activity, but not IRES activity. Assessment of SNV translation initiation in the natural context of the provirus determined that SNV is reliant on a cap-dependent initiation mechanism. Experiments with siRNAs identified that REV-A and HTLV-1 PCE modulate post-transcriptional gene expression through interaction with host RNA helicase A (RHA). Analysis of hybrid SNV/HTLV-1 proviruses determined SNV PCE facilitates Rex/Rex responsive element-independent Gag production and interaction with RHA is necessary. Ribosomal profile analyses determined that RHA is necessary for polysome association of HTLV-1 gag and provide direct evidence that RHA is necessary for efficient HTLV-1 replication. We conclude that PCE/RHA is an important translation regulatory axis of multiple lymphotropic retroviruses. We speculate divergent retroviruses have evolved a convergent RNA–protein interaction to modulate translation of their highly structured mRNA

Targeting inflammation to reduce cardiovascular disease risk: a realistic clinical prospect?

Data from basic science experiments is overwhelmingly supportive of the causal role of immune-inflammatory response(s) at the core of atherosclerosis, and therefore the theoretical potential to manipulate the inflammatory response to prevent cardiovascular events. However, extrapolation to humans requires care and we still lack definitive evidence to show that interfering in immune-inflammatory processes may safely lessen clinical atherosclerosis. In this review, we discuss key therapeutic targets in the treatment of vascular inflammation, placing basic research in to a wider clinical perspective, as well as identifying outstanding questions

ILC3s restrict the dissemination of intestinal bacteria to safeguard liver regeneration after surgery.

It is generally believed that environmental or cutaneous bacteria are the main origin of surgical infections. Therefore, measures to prevent postoperative infections focus on optimizing hygiene and improving asepsis and antisepsis. In a large cohort of patients with infections following major surgery, we identified that the causative bacteria are mainly of intestinal origin. Postoperative infections of intestinal origin were also found in mice undergoing partial hepatectomy. CCR6+ group 3 innate lymphoid cells (ILC3s) limited systemic bacterial spread. Such bulwark function against host invasion required the production of interleukin-22 (IL-22), which controlled the expression of antimicrobial peptides in hepatocytes, thereby limiting bacterial spread. Using genetic loss-of-function experiments and punctual depletion of ILCs, we demonstrate that the failure to restrict intestinal commensals by ILC3s results in impaired liver regeneration. Our data emphasize the importance of endogenous intestinal bacteria as a source for postoperative infection and indicate ILC3s as potential new targets

Investigating interactions between epicardial adipose tissue and cardiac myocytes: what can we learn from different approaches?

Heart disease is a major cause of morbidity and mortality throughout the world. Some cardiovascular conditions can be modulated by lifestyle factors such as increased exercise or a healthier diet, but many require surgical or pharmacological interventions for their management. More targeted and less invasive therapies would be beneficial. Recently it has become apparent that epicardial adipose tissue plays an important role in normal and pathological cardiac function, and it is now the focus of considerable research. Epicardial adipose tissue can be studied by imaging of various kinds, and these approaches have yielded much useful information. However at a molecular level it is more difficult to study as it is relatively scarce in animal models and, for practical and ethical reasons, not always available in sufficient quantities from patients. What is needed is a robust model system in which the interactions between epicardial adipocytes and cardiac myocytes can be studied, and physiologically relevant manipulations performed. There are drawbacks to conventional culture methods, not least the difficulty of culturing both cardiac myocytes and adipocytes, each of which has special requirements. We discuss the benefits of a three-dimensional co-culture model in which in vivo interactions can be replicated

Interobserver Agreement in Vascular Invasion Scoring and the Added Value of Immunohistochemistry for Vascular Markers to Predict Disease Relapse in Stage I Testicular Nonseminomas

Vascular invasion has been identified as an informative risk factor for relapse in stage I testicular nonseminomas, used to tailor treatment. We investigated interobserver agreement in vascular invasion reporting and studied the potential additional value of immunohistochemistry for vascular markers for predicting relapse. Patients (n=52) with stage I testicular nonseminomas undergoing surveillance (1993-2006) were included (median follow-up of 66 mo). Two formalin-fixed paraffin-embedded blocks with >1 cm tissue and tumor/normal parenchyma interface were stained with hematoxylin and eosin and CD31, FVIII, and D2-40. Slides were assessed by 3 independent testicular germ cell tumor-dedicated pathologists, and agreement was assessed using Cohen κ statistic. Sensitivity, specificity, and accuracy of vascular invasion scoring in predicting relapse were calculated. Agreement among testicular germ cell tumor-dedicated pathologists was moderate (κ=0.49 to 0.54), as was performance in predicting disease relapse (particularly, specificity of 86%). Immunohistochemistry increased overall sensitivity (71%), but decreased specificity (71%) in predicting relapse. All patients (n=8) with both blood and lymphatic vascular invasion developed a relapse. In multivariable analysis (including age, tumor size, rete testis invasion, and serum tumor markers), only vascular invasion had an independent impact in predicting relapse. Assessment of vascular invasion by testicular germ cell tumor-dedicated pathologists is good and is clinically meaningful, predicting disease relapse. Immunohistochemistry for vascular markers improves sensitivity of detecting disease relapse and allows for the identification of high-risk patients with both blood and lymphatic vascular invasion simultaneously, potentially of interest for tailored chemotherapy