The REconsolidaTion Using RewiNd Study (RETURN): trial protocol

Background: An increasing body of research highlights reconsolidation-based therapies as emerging treatments for post-traumatic stress disorder (PTSD). The Rewind Technique is a non-pharmacological reconsolidation-based therapy with promising early results, which now requires evaluation through an RCT. Objectives: This is a preliminary efficacy RCT to determine if the Rewind Technique is likely to be a good candidate to test against usual care in a future pragmatic efficacy RCT. Methods: 40 participants will be randomised to receive either the Rewind Technique immediately, or after an 8 week wait. The primary outcome will be PTSD symptom severity as measured by the Clinician-Administered PTSD Scale for DSM5 (CAPS-5) at 8 and 16 weeks post-randomisation. Secondary outcome measures include the PTSD Checklist (PCL-5), International Trauma Questionnaire (ITQ), Patient Health Questionnaire (PHQ-9), the General Anxiety Disorder-7 (GAD-7), Insomnia Severity Index, the Euro-Qol-5D (EQ5D-5 L), the prominence of re-experiencing specific symptoms (CAPS-5) and an intervention acceptability questionnaire to measure tolerability of the intervention. Conclusions: This study will be the first RCT to assess the Rewind Technique. Using a cross-over methodology we hope to rigorously assess the efficacy and tolerability of Rewind using pragmatic inclusion criteria. Potential challenges include participant recruitment and retention

NLP meets psychotherapy: Using predicted client emotions and self-reported client emotions to measure emotional coherence

Emotions are experienced and expressed through various response systems. Coherence between emotional experience and emotional expression is considered important to clients' well being. To date, emotional coherence (EC) has been studied at a single time point using lab-based tasks with relatively small datasets. No study has examined EC between the subjective experience of emotions and emotion expression in therapy or whether this coherence is associated with clients' well being. Natural language Processing (NLP) approaches have been applied to identify emotions from psychotherapy dialogue, which can be implemented to study emotional processes on a larger scale. However, these methods have yet to be used to study coherence between emotional experience and emotional expression over the course of therapy and whether it relates to clients' well-being. This work presents an end-to-end approach where we use emotion predictions from our transformer based emotion recognition model to study emotional coherence and its diagnostic potential in psychotherapy research. We first employ our transformer based approach on a Hebrew psychotherapy dataset to automatically label clients' emotions at utterance level in psychotherapy dialogues. We subsequently investigate the emotional coherence between clients' self-reported emotional states and our model-based emotion predictions. We also examine the association between emotional coherence and clients' well being. Our findings indicate a significant correlation between clients' self-reported emotions and positive and negative emotions expressed verbally during psychotherapy sessions. Coherence in positive emotions was also highly correlated with clients well-being. These results illustrate how NLP can be applied to identify important emotional processes in psychotherapy to improve diagnosis and treatment for clients suffering from mental-health problems.Comment: Accepted at Empowering Communities: A Participatory Approach to AI for Mental Health, NeurIPS 2022 VIRTUAL Worksho

Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation

Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.e., the performance of old relations seriously degrades when the model learns new relations. Most previous work attributes catastrophic forgetting to the corruption of the learned representations as new relations come, with an implicit assumption that the CRE models have adequately learned the old relations. In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process. To address this issue, we encourage the model to learn more precise and robust representations through a simple yet effective adversarial class augmentation mechanism (ACA), which is easy to implement and model-agnostic. Experimental results show that ACA can consistently improve the performance of state-of-the-art CRE models on two popular benchmarks.Comment: Accepted by EMNLP 202

Post Traumatic Stress Disorder and Substance Use Disorder as Two Pathologies Affecting Memory Reactivation: Implications for New Therapeutic Approaches

In the present review, we provide evidence indicating that although post traumatic stress disorder (PTSD) and substance use disorder (SUD) are two distinct pathologies with very different impacts on people affected by these chronic illnesses, they share numerous common characteristics, present high rates of co-morbidity, and may result from common physiological dysfunctions. We propose that these pathologies result from hyper reactivity to reminders, and thus should be considered as two disorders of memory, treated as such. We review the different possibilities to intervene on pathological memories such as extinction therapy and reconsolidation blockade. We also introduce new therapeutic avenues directly indicate by our recent proposal to replace the consolidation/reconsolidation hypothesis by the integration concept. State dependency and emotional remodeling are two innovative treatments that have already provided encouraging results. In summary, this review shows that the discovery of reactivation-dependent memory malleability has open new therapeutic avenues based on the reprocessing of pathological memories, which constitute promising approaches to treat PTSD and SUD

Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection

Lifelong event detection aims to incrementally update a model with new event types and data while retaining the capability on previously learned old types. One critical challenge is that the model would catastrophically forget old types when continually trained on new data. In this paper, we introduce Episodic Memory Prompts (EMP) to explicitly preserve the learned task-specific knowledge. Our method adopts continuous prompt for each task and they are optimized to instruct the model prediction and learn event-specific representation. The EMPs learned in previous tasks are carried along with the model in subsequent tasks, and can serve as a memory module that keeps the old knowledge and transferring to new tasks. Experiment results demonstrate the effectiveness of our method. Furthermore, we also conduct a comprehensive analysis of the new and old event types in lifelong learning.Comment: Accepted to COLING'22 Main Conference (Short paper). 9 pages, 2 figures, 3 table

Data Mining to find out the patterns in the data of circulation section log of July, 2022 of Jamia Millia Islamia University library using python

Data mining is a process that is used to find the important and meaningful patterns in the large data-set. It is used to convert the raw data into meaningful information and knowledge. The unknown patterns are extracted from the unstructured data which is stored in proper format and then utilized for developing future strategies. Various questions arise such as what is data mining, its process, tools or software used in the data mining, and what are the applications of data mining in libraries? This article describes the basics of data mining, process, tools and techniques used in data mining. The data of the circulation section of Jamia Millia University is taken to find out the meaningful patterns and to know certain borrowing habits of the patrons of the library. Python as a language is used for the coding for the determination of various helpful sequences which provide a meaningful interpretation for the circulation section log and such kinds of patterns can be used for the decision making for the library

Class-Incremental Learning based on Label Generation

Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, CF is drastically reduced and the generalizable representations of pre-trained models can be better retained. We thus propose a new CIL method (VAG) that also leverages the sparsity of vocabulary to focus the generation and creates pseudo-replay samples by using label semantics. Experimental results show that VAG outperforms baselines by a large margin.Comment: 12 pages, ACL 2023 Main Conferenc

Orthogonal Subspace Learning for Language Model Continual Learning

Benefiting from massive corpora and advanced hardware, large language models (LLMs) exhibit remarkable capabilities in language understanding and generation. However, their performance degrades in scenarios where multiple tasks are encountered sequentially, also known as catastrophic forgetting. In this paper, we propose orthogonal low-rank adaptation (O-LoRA), a simple and efficient approach for continual learning in language models, effectively mitigating catastrophic forgetting while learning new tasks. Specifically, O-LoRA learns tasks in different (low-rank) vector subspaces that are kept orthogonal to each other in order to minimize interference. Our method induces only marginal additional parameter costs and requires no user data storage for replay. Experimental results on continual learning benchmarks show that our method outperforms state-of-the-art methods. Furthermore, compared to previous approaches, our method excels in preserving the generalization ability of LLMs on unseen tasks.Comment: EMNLP 2023 finding