From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue

Emotion recognition in conversations (ERC) is a crucial task for building human-like conversational agents. While substantial efforts have been devoted to ERC for chit-chat dialogues, the task-oriented counterpart is largely left unattended. Directly applying chit-chat ERC models to task-oriented dialogues (ToDs) results in suboptimal performance as these models overlook key features such as the correlation between emotions and task completion in ToDs. In this paper, we propose a framework that turns a chit-chat ERC model into a task-oriented one, addressing three critical aspects: data, features and objective. First, we devise two ways of augmenting rare emotions to improve ERC performance. Second, we use dialogue states as auxiliary features to incorporate key information from the goal of the user. Lastly, we leverage a multi-aspect emotion definition in ToDs to devise a multi-task learning objective and a novel emotion-distance weighted loss function. Our framework yields significant improvements for a range of chit-chat ERC models on EmoWOZ, a large-scale dataset for user emotion in ToDs. We further investigate the generalisability of the best resulting model to predict user satisfaction in different ToD datasets. A comparison with supervised baselines shows a strong zero-shot capability, highlighting the potential usage of our framework in wider scenarios.Comment: Accepted by SIGDIAL 202

EmoUS: Simulating User Emotions in Task-Oriented Dialogues

Existing user simulators (USs) for task-oriented dialogue systems only model user behaviour on semantic and natural language levels without considering the user persona and emotions. Optimising dialogue systems with generic user policies, which cannot model diverse user behaviour driven by different emotional states, may result in a high drop-off rate when deployed in the real world. Thus, we present EmoUS, a user simulator that learns to simulate user emotions alongside user behaviour. EmoUS generates user emotions, semantic actions, and natural language responses based on the user goal, the dialogue history, and the user persona. By analysing what kind of system behaviour elicits what kind of user emotions, we show that EmoUS can be used as a probe to evaluate a variety of dialogue systems and in particular their effect on the user's emotional state. Developing such methods is important in the age of large language model chat-bots and rising ethical concerns.Comment: accepted by SIGIR202

CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation

Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks. The quality of annotations tends to deteriorate with the transition from expert-based to crowd-sourced labelling. To address these challenges, we present \textbf{CAMELL} (Confidence-based Acquisition Model for Efficient self-supervised active Learning with Label validation), a pool-based active learning framework tailored for sequential multi-output problems. CAMELL possesses three core features: (1) it requires expert annotators to label only a fraction of a chosen sequence, (2) it facilitates self-supervision for the remainder of the sequence, and (3) it employs a label validation mechanism to prevent erroneous labels from contaminating the dataset and harming model performance. We evaluate CAMELL on sequential tasks, with a special emphasis on dialogue belief tracking, a task plagued by the constraints of limited and noisy datasets. Our experiments demonstrate that CAMELL outperforms the baselines in terms of efficiency. Furthermore, the data corrections suggested by our method contribute to an overall improvement in the quality of the resulting datasets

ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?

Recent research on dialogue state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. However, performance gains heavily depend on aggressive data augmentation and fine-tuning of ever larger language model based architectures. In contrast, general purpose language models, trained on large amounts of diverse data, hold the promise of solving any kind of task without task-specific training. We present preliminary experimental results on the ChatGPT research preview, showing that ChatGPT achieves state-of-the-art performance in zero-shot DST. Despite our findings, we argue that properties inherent to general purpose models limit their ability to replace specialized systems. We further theorize that the in-context learning capabilities of such models will likely become powerful tools to support the development of dedicated and dynamic dialogue state trackers.Comment: 13 pages, 3 figures, accepted at ACL 202

Multiscale spatial modeling with applications in image analysis

Computer vision is a very important research area and is continuously growing. One of the prevalent research areas in computer vision is image matching. In image matching there are two main components, namely feature detection and feature matching. The aim of this this study is to determine whether Direct Sampling can be used for feature matching, and also if the combination of Direct Sampling and the Discrete Pulse Transform feature detector can be a successful image matching tool. In feature detection there are many strong methods including convolutional neural networks and scale-space models such as SIFT and SURF, which are very well-known feature detection algorithms. In this work we utilize another scale-space decomposition tool called the Discrete Pulse Transform (DPT). We particularly use the DPT decomposition to enable significant feature detection. We then concentrate on using the Direct Sampling algorithm, a stochastic spatial simulation algorithm, for modelling and matching of features. We do not consider convolutional neural networks or SIFT or SURF for texture matching in this work, this is because we particularly focus on the use of spatial statistics in image matching. We finally propose a novel multiscale spatial statistics feature detection and matching algorithm which combines the DPT feature detection with Direct Sampling for feature matching, specifically for texture classes of images. The performance of the proposed method is tested by comparing the distances obtained from the proposed algorithm between different texture images. We see that this proposed novel multiscale spatial modelling approach to feature matching with the focus on textures performs well at discriminating between difficult to discriminate between textures.Dissertation (MSc)--University of Pretoria, 2018.NRF SASA GrantStatistics HUB InternshipCAIR Fund, CSIRStatisticsMScUnrestricte

Training for shorter ultra-trail races results in a higher injury rate, a more diverse injury profile, and more severe injuries : 2022 Mac ultra races

DATA STATEMENT : The research data related to this study will be made available upon reasonable request.OBJECTIVES : Determine and compare the epidemiology, clinical characteristics, and injury severity among race entrants training towards different ultra-trail race distances. DESIGN : Retrospective cross-sectional study. SETTING : The six months training period before the 2022 Mac Ultra races (46 km, 80 km, 161 km and 322 km). PARTICIPANTS : Of the 245 race entrants, 162 (66% of Mac ultra-trail runners) consented to analyse their data. OUTCOME MEASURES : Injury rate (injuries per 1000 h of running), point prevalence (% of currently injured participants), injury severity (time loss), and the frequency (n, %) of injuries reported during pre-race medical screening in the six months before the race. Using inferential statistics, we compared the injury rates between the different race distance categories (46 km, 80 km, 161 km, 322 km). All tests were performed at a 5% level of significance. RESULTS : We reported a statistically significantly higher injury rate among 46 km study participants (3.09 injuries per 1000 h) compared to the injury rates reported among 80 km (0.68 injuries per 1000 h; p = 0.001) and 161 km (1.09 injuries per 1000 h; p = 0.028) participants. The lower limb (89%) was the most injured anatomical region, with only 46 km study participants reporting upper limb, trunk, and head injuries (11%). Muscle/tendon was the most reported injured tissue type (56%), with muscle injuries (31%) the most reported pathology type. Shorter distance ultra-trail runners reported the highest injury severity. CONCLUSION : Ultra-trail runners training towards shorter ultra-trail distance races presented with a higher injury rate, more diverse injury profile, and a higher injury severity.https://www.elsevier.com/ptsp2024-11-16hj2023PhysiotherapySports MedicineStatisticsSDG-03:Good heatlh and well-bein