An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss

Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an end-to-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses.Comment: AAAI-1

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition

Keyword-Guided Neural Conversational Model

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.Comment: AAAI-202

Towards Persona-Based Empathetic Conversational Models

Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in empathetic conversations. To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for persona-based empathetic conversations. We then propose CoBERT, an efficient BERT-based response selection model that obtains the state-of-the-art performance on our dataset. Finally, we conduct extensive experiments to investigate the impact of persona on empathetic responding. Notably, our results show that persona improves empathetic responding more when CoBERT is trained on empathetic conversations than non-empathetic ones, establishing an empirical link between persona and empathy in human conversations.Comment: Accepted to EMNLP 2020 (A new dataset is proposed: https://github.com/zhongpeixiang/PEC

CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts

Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.Comment: AAAI-202

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The draft genome of watermelon (Citrullus lanatus) and resequencing of 20 diverse accessions

Watermelon, Citrullus lanatus, is an important cucurbit crop grown throughout the world. Here we report a high-quality draft genome sequence of the east Asia watermelon cultivar 97103 (2n = 2 7 = 22) containing 23,440 predicted protein-coding genes. Comparative genomics analysis provided an evolutionary scenario for the origin of the 11 watermelon chromosomes derived from a 7-chromosome paleohexaploid eudicot ancestor. Resequencing of 20 watermelon accessions representing three different C. lanatus subspecies produced numerous haplotypes and identified the extent of genetic diversity and population structure of watermelon germplasm. Genomic regions that were preferentially selected during domestication were identified. Many disease-resistance genes were also found to be lost during domestication. In addition, integrative genomic and transcriptomic analyses yielded important insights into aspects of phloem-based vascular signaling in common between watermelon and cucumber and identified genes crucial to valuable fruit-quality traits, including sugar accumulation and citrulline metabolism

Development of 3D indoor positioning and navigation system

As the age of Internet of Things comes, the location and context based services and applications become increasingly popular. These services and applications have already been applied in many existing scenarios such as emergence evacuation, museums and shopping malls. Despite that this area has been extensively researched and many services and applications have been implemented, technologies such as GPS and Wi-Fi lack the positioning accuracy and consume lots of power in semi-outdoor environment where signals are attenuated by walls and floors. In addition, the existing navigation system can only be applied in 2D environment, whereas most of the modern buildings or shopping malls have multiple floors. Moreover, the compatibility issues of existing mobile positioning applications due to their high customization levels make these applications difficult to be used in other buildings or scenarios. In this project, we leverage Bluetooth Low Energy (BLE) based iBeacon technology and 3D A* pathfinding algorithm to propose an all-in-one framework for mobile applications to provide users consistent positioning and navigation services. The BLE based iBeacon technology provides power efficient solution to locate users. The 3D A* algorithm can navigate users to different floors or buildings with minimum amount of processing time. The all-in-one framework provides users a solution to the compatibility issue so that one mobile positioning application can be used in any buildings. This positioning and navigation system is implemented and tested comprehensively. The positioning accuracy is tested to be around 1.8 meters. The performance of the navigation algorithm is also simulated and tested with superior results in terms of processing time. The use case studies are conducted successfully as well to examine the navigation service in real scenarios.Bachelor of Engineerin

Towards humanized open-domain conversational agents

Language is the hallmark of humanity. Conversation or dialogue is a fundamental arena of language and one of the most commonly used forms by humans. In the field of artificial intelligence (AI) or, more specifically, natural language processing (NLP), a conversational agent (CA), also known as a dialogue system (DS), is an intelligent machine that can converse with humans in natural language. There are primarily three types of CAs: task-oriented CAs, question-answering (QA) systems, and open-domain CAs. In this thesis, we focus on open-domain CAs, also known as chatbots, which are designed to chat with users in any topics engagingly with the aim of establishing long-term relationships. Open-domain CAs are essential in modern conversational user interfaces and have been adopted in numerous business domains such as personal assistant, customer support, education, and healthcare. Building a human-level open-domain CA has been one of the major milestones in AI research. However, existing open-domain CAs often fail to model the intrinsic traits of humans and exhibit the following limitations: 1) they lack emotional intelligence and cannot generate or recognize emotions in conversations, which often lead to dull or generic responses; 2) they lack commonsense knowledge and often produce incoherent or unrelated responses; 3) they lack persona and often produce inconsistent responses; and 4) they lack empathy and often produce non-empathetic responses. Addressing the aforementioned limitations is important for bridging the gap between existing CAs and human-level CAs. These intrinsic traits of humans have been empirically shown to improve the performance of CAs on various tasks, e.g., user satisfaction in customer support, user trust and engagement in education, and mental health of participants in healthcare. Humanization is the process of attributing human traits to an entity. In this thesis, we propose to address the limitations by humanizing open-domain CAs with the following human traits: emotion, commonsense, persona, and empathy. Our thesis makes a step towards humanized open-domain CAs. Specifically, to humanize CAs with emotion and commonsense, we first propose an emotional open-domain CA that can generate natural and emotional responses. We then incorporate commonsense into emotional CAs and propose a conversational emotion recognition model and a commonsense-aware emotional response generation model. Experimental results show that both emotion and commonsense improve response quality and human ratings. In addition, emotion and commonsense are shown to have complementary effects in conversational emotion recognition and generation. To humanize CAs with persona and empathy, we propose a persona-based empathetic CA and investigate the impact of persona and empathy on response quality. Experimental results show that both persona and empathy consistently improve response quality and human ratings. In addition, we investigate the impact of persona on empathetic responding and our results suggest that persona has a larger impact on empathetic conversations than non-empathetic ones. Finally, we propose a humanized open-domain CA (HCA) that possesses all the proposed human traits simultaneously: emotion, commonsense, persona, and empathy. HCA aims to address the aforementioned limitations altogether. Specifically, we adopt a pretrain-and-finetune paradigm to develop a retrieval-based HCA in a multi-task learning setting. Experimental results show that the multi-task performance of HCA is better than its single-task performance, and our HCA outperforms the state-of-the-art CAs for response retrieval across multiple evaluation datasets. Our case study shows that our proposed HCA can demonstrate multiple human traits and produce consistent, informative, and empathetic responses.Doctor of Philosoph