Modeling Empathy and Distress in Reaction to News Stories

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.Comment: To appear at EMNLP 201

Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory

Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.Comment: Accepted in AAAI 201

Computational modeling of turn-taking dynamics in spoken conversations

The study of human interaction dynamics has been at the center for multiple research disciplines in- cluding computer and social sciences, conversational analysis and psychology, for over decades. Recent interest has been shown with the aim of designing computational models to improve human-machine interaction system as well as support humans in their decision-making process. Turn-taking is one of the key aspects of conversational dynamics in dyadic conversations and is an integral part of human- human, and human-machine interaction systems. It is used for discourse organization of a conversation by means of explicit phrasing, intonation, and pausing, and it involves intricate timing. In verbal (e.g., telephone) conversation, the turn transitions are facilitated by inter- and intra- speaker silences and over- laps. In early research of turn-taking in the speech community, the studies include durational aspects of turns, cues for turn yielding intention and lastly designing turn transition modeling for spoken dia- log agents. Compared to the studies of turn transitions very few works have been done for classifying overlap discourse, especially the competitive act of overlaps and function of silences. Given the limitations of the current state-of-the-art, this dissertation focuses on two aspects of con- versational dynamics: 1) design automated computational models for analyzing turn-taking behavior in a dyadic conversation, 2) predict the outcome of the conversations, i.e., observed user satisfaction, using turn-taking descriptors, and later these two aspects are used to design a conversational profile for each speaker using turn-taking behavior and the outcome of the conversations. The analysis, experiments, and evaluation has been done on a large dataset of Italian call-center spoken conversations where customers and agents are engaged in real problem-solving tasks. Towards solving our research goal, the challenges include automatically segmenting and aligning speakers’ channel from the speech signal, identifying and labeling the turn-types and its functional aspects. The task becomes more challenging due to the presence of overlapping speech. To model turn- taking behavior, the intension behind these overlapping turns needed to be considered. However, among all, the most critical question is how to model observed user satisfaction in a dyadic conversation and what properties of turn-taking behavior can be used to represent and predict the outcome. Thus, the computational models for analyzing turn-taking dynamics, in this dissertation includes au- tomatic segmenting and labeling turn types, categorization of competitive vs non-competitive overlaps, silences (e.g., lapse, pauses) and functions of turns in terms of dialog acts. The novel contributions of the work presented here are to 1. design of a fully automated turn segmentation and labeling (e.g., agent vs customer’s turn, lapse within the speaker, and overlap) system. 2. the design of annotation guidelines for segmenting and annotating the speech overlaps with the competitive and non-competitive labels. 3. demonstrate how different channels of information such as acoustic, linguistic, and psycholin- guistic feature sets perform in the classification of competitive vs non-competitive overlaps. 4. study the role of speakers and context (i.e., agents’ and customers’ speech) for conveying the information of competitiveness for each individual feature set and their combinations. 5. investigate the function of long silences towards the information flow in a dyadic conversation. The extracted turn-taking cues is then used to automatically predict the outcome of the conversation, which is modeled from continuous manifestations of emotion. The contributions include 1. modeling the state of the observed user satisfaction in terms of the final emotional manifestation of the customer (i.e., user). 2. analysis and modeling turn-taking properties to display how each turn type influence the user satisfaction. 3. study of how turn-taking behavior changes within each emotional state. Based on the studies conducted in this work, it is demonstrated that turn-taking behavior, specially competitiveness of overlaps, is more than just an organizational tool in daily human interactions. It represents the beneficial information and contains the power to predict the outcome of the conversation in terms of satisfaction vs not-satisfaction. Combining the turn-taking behavior and the outcome of the conversation, the final and resultant goal is to design a conversational profile for each speaker. Such profiled information not only facilitate domain experts but also would be useful to the call center agent in real time. These systems are fully automated and no human intervention is required. The findings are po- tentially relevant to the research of overlapping speech and automatic analysis of human-human and human-machine interactions

Natural Language Processing for Motivational Interviewing Counselling: Addressing Challenges in Resources, Benchmarking and Evaluation

Motivational interviewing (MI) is a counselling style often used in healthcare to improve patient health and quality of life by promoting positive behaviour changes. Natural language processing (NLP) has been explored for supporting MI use cases of insights/feedback generation and therapist training, such as automatically assigning behaviour labels to therapist/client utterances and generating possible therapist responses. Despite the progress of NLP for MI applications, significant challenges remain. The most prominent one is the lack of publicly available and annotated MI dialogue corpora due to privacy constraints. Consequently, there is also a lack of common benchmarks and poor reproducibility across studies. Furthermore, human evaluation for therapist response generation is expensive and difficult to scale due to its dependence on MI experts as evaluators. In this thesis, we address these challenges in 4 directions: low-resource NLP modelling, MI dialogue dataset creation, benchmark development for real-world applicable tasks, and laypeople-experts human evaluation study. First, we explore zero-shot binary empathy assessment at the utterance level. We experiment with a supervised approach that trains on heuristically constructed empathy vs. non-empathy contrast in non-therapy dialogues. While this approach has better performance than other models without empathy-aware training, it is still suboptimal and therefore highlights the need for a well-annotated MI dataset. Next, we create AnnoMI, the first publicly available dataset of expert-annotated MI dialogues. It contains MI conversations that demonstrate both high- and low-quality counselling, with extensive annotations by domain experts covering key MI attributes. We also conduct comprehensive analyses of the dataset. Then, we investigate two AnnoMI-based real-world applicable tasks: predicting current-turn therapist/client behaviour given the utterance, and forecasting next-turn therapist behaviour given the dialogue history. We find that language models (LMs) perform well on predicting therapist behaviours with good generalisability to new dialogue topics. However, LMs have suboptimal forecasting performance, which reflects therapists' flexibility where multiple optimal next-turn actions are possible. Lastly, we ask both laypeople and experts to evaluate the generation of a crucial type of therapist responses -- reflection -- on a key quality aspect: coherence and context-consistency. We find that laypeople are a viable alternative to experts, as laypeople show good agreement with each other and correlation with experts. We also find that a large LM generates mostly coherent and consistent reflections. Overall, the work of this thesis broadens access to NLP for MI significantly as well as presents a wide range of findings on related natural language understanding/generation tasks with a real-world focus. Thus, our contributions lay the groundwork for the broader NLP community to be more engaged in research for MI, which will ultimately improve the quality of life for recipients of MI counselling