Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEffective Social Chatbot Strategies for Increasing User Initiative
استراتيجيات Chatbot الاجتماعية الفعالة لزيادة مبادرة المستخدم
358
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
لا تدعم العديد من Chatbots الموجودة بشكل فعال المبادرة المختلطة، مما أجبر مستخدميهم على الاستجابة بشكل سلبي أو يؤدي باستمرار. نسعى لتحسين هذه التجربة من خلال تقديم آليات جديدة لتشجيع مبادرة المستخدم في محادثات Chatbot الاجتماعية. نظرا لأن مبادرة المستخدم في هذا الإعداد متميزة عن المبادرة في الحوار البشري البشري أو موجه نحو المهام، فإننا نقترح أولا تعريف جديد يمثل المستخدمين من فريدة من الفريدين للمستخدمين في هذا السياق. بالتقييم من اللغويات، نقترح ثلاث آليات لتعزيز مبادرة المستخدم: التوجيه الخلفي، والإفصاح الشخصي، واستبدال الأسئلة ببيانات. نظهر أن المقاييس التلقائية البسيطة لطول الكلام وعدد عبارات الأسماء وتنوع ردود المستخدم يرتبط بالحكم البشري للمبادرة. أخيرا، نستخدم هذه المقاييس تشير إلى أن هذه الاستراتيجيات تؤدي إلى زيادات ذات دلالة إحصائية في مبادرة المستخدم، حيث تكرارها، ولكن غير مفرطة، هي الإستراتيجية الأكثر فعالية.
Many existing chatbots do not effectively support mixed initiative, forcing their users to either respond passively or lead constantly. We seek to improve this experience by introducing new mechanisms to encourage user initiative in social chatbot conversations. Since user initiative in this setting is distinct from initiative in human-human or task-oriented dialogue, we first propose a new definition that accounts for the unique behaviors users take in this context. Drawing from linguistics, we propose three mechanisms to promote user initiative: back-channeling, personal disclosure, and replacing questions with statements. We show that simple automatic metrics of utterance length, number of noun phrases, and diversity of user responses correlate with human judgement of initiative. Finally, we use these metrics to suggest that these strategies do result in statistically significant increases in user initiative, where frequent, but not excessive, back-channeling is the most effective strategy.
References used
https://aclanthology.org/
rate research
Read More
Artificial intelligence chatbots are the vanguard in technology-based intervention to change people's behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This wor
k introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants' changes in the barrier and social support for physical activity. We also analyzed how participant's baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention chatbot.
Neural generative dialogue agents have shown an increasing ability to hold short chitchat conversations, when evaluated by crowdworkers in controlled settings. However, their performance in real-life deployment -- talking to intrinsically-motivated u
sers in noisy environments -- is less well-explored. In this paper, we perform a detailed case study of a neural generative model deployed as part of Chirpy Cardinal, an Alexa Prize socialbot. We find that unclear user utterances are a major source of generative errors such as ignoring, hallucination, unclearness and repetition. However, even in unambiguous contexts the model frequently makes reasoning errors. Though users express dissatisfaction in correlation with these errors, certain dissatisfaction types (such as offensiveness and privacy objections) depend on additional factors -- such as the user's personal attitudes, and prior unaddressed dissatisfaction in the conversation. Finally, we show that dissatisfied user utterances can be used as a semi-supervised learning signal to improve the dialogue system. We train a model to predict next-turn dissatisfaction, and show through human evaluation that as a ranking function, it selects higher-quality neural-generated utterances.
Most chatbot literature that focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more human-like. However, very little work delves into what really separates humans from chatbots -- humans intrinsically unders
tand the effect their responses have on the interlocutor and often respond with an intention such as proposing an optimistic view to make the interlocutor feel better. This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans. The guiding chatbot is assigned an intention and learns to induce the interlocutor to reply with responses matching the intention, for example, long responses, joyful responses, responses with specific words, etc. We examined our framework using three experimental setups and evaluated the guiding chatbot with four different metrics to demonstrate flexibility and performance advantages. Additionally, we performed trials with human interlocutors to substantiate the guiding chatbot's effectiveness in influencing the responses of humans to a certain extent. Code will be made available to the public.
Amidst rising mental health needs in society, virtual agents are increasingly deployed in counselling. In order to give pertinent advice, counsellors must first gain an understanding of the issues at hand by eliciting sharing from the counsellee. It
is thus important for the counsellor chatbot to encourage the user to open up and talk. One way to sustain the conversation flow is to acknowledge the counsellee's key points by restating them, or probing them further with questions. This paper applies models from two closely related NLP tasks --- summarization and question generation --- to restatement and question generation in the counselling context. We conducted experiments on a manually annotated dataset of Cantonese post-reply pairs on topics related to loneliness, academic anxiety and test anxiety. We obtained the best performance in both restatement and question generation by fine-tuning BertSum, a state-of-the-art summarization model, with the in-domain manual dataset augmented with a large-scale, automatically mined open-domain dataset.
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identifica
tion of users at risk. In this work, we propose a framework jointly leveraging a user's emotional history and social information from a user's neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a time-sensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
