Jewelry Shop Conversational Chatbot
- URL: http://arxiv.org/abs/2206.04659v1
- Date: Thu, 9 Jun 2022 17:56:51 GMT
- Title: Jewelry Shop Conversational Chatbot
- Authors: Safa Zaid, Aswah Malik, Kisa Fatima
- Abstract summary: We build a conversational agent for a jewelry shop that finds the underlying objective of the customer's query by finding similarity of the input to patterns in the corpus.
Our system features an audio input interface for clients, so they may speak to it in natural language.
To gauge the system's performance, we used performance metrics such as Recall, Precision and F1 score.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Since the advent of chatbots in the commercial sector, they have been widely
employed in the customer service department. Typically, these commercial
chatbots are retrieval-based, so they are unable to respond to queries absent
in the provided dataset. On the contrary, generative chatbots try to create the
most appropriate response, but are mostly unable to create a smooth flow in the
customer-bot dialog. Since the client has few options left for continuing after
receiving a response, the dialog becomes short. Through our work, we try to
maximize the intelligence of a simple conversational agent so it can answer
unseen queries, and generate follow-up questions or remarks. We have built a
chatbot for a jewelry shop that finds the underlying objective of the
customer's query by finding similarity of the input to patterns in the corpus.
Our system features an audio input interface for clients, so they may speak to
it in natural language. After converting the audio to text, we trained the
model to extract the intent of the query, to find an appropriate response and
to speak to the client in a natural human voice. To gauge the system's
performance, we used performance metrics such as Recall, Precision and F1
score.
Related papers
- Self-Directed Turing Test for Large Language Models [56.64615470513102]
The Turing test examines whether AIs can exhibit human-like behaviour in natural language conversations.
Traditional Turing tests adopt a rigid dialogue format where each participant sends only one message each time.
This paper proposes the Self-Directed Turing Test, which extends the original test with a burst dialogue format.
arXiv Detail & Related papers (2024-08-19T09:57:28Z) - Can Language Models Learn to Listen? [96.01685069483025]
We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words.
Our approach autoregressively predicts a response of a listener: a sequence of listener facial gestures, quantized using a VQ-VAE.
We show that our generated listener motion is fluent and reflective of language semantics through quantitative metrics and a qualitative user study.
arXiv Detail & Related papers (2023-08-21T17:59:02Z) - Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on
Self-Chat Data [101.63682141248069]
Chat models, such as ChatGPT, have shown impressive capabilities and have been rapidly adopted across numerous domains.
We propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT.
We employ parameter-efficient tuning to enhance LLaMA, an open-source large language model.
arXiv Detail & Related papers (2023-04-03T17:59:09Z) - Search-Engine-augmented Dialogue Response Generation with Cheaply
Supervised Query Production [98.98161995555485]
We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation.
As the core module, a query producer is used to generate queries from a dialogue context to interact with a search engine.
Experiments show that our query producer can achieve R@1 and R@5 rates of 62.4% and 74.8% for retrieving gold knowledge.
arXiv Detail & Related papers (2023-02-16T01:58:10Z) - Leveraging Large Language Models to Power Chatbots for Collecting User
Self-Reported Data [15.808841433843742]
Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts.
We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably.
arXiv Detail & Related papers (2023-01-14T07:29:36Z) - A Deep Learning Approach to Integrate Human-Level Understanding in a
Chatbot [0.4632366780742501]
Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second.
We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence.
arXiv Detail & Related papers (2021-12-31T22:26:41Z) - Training Conversational Agents with Generative Conversational Networks [74.9941330874663]
We use Generative Conversational Networks to automatically generate data and train social conversational agents.
We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data.
arXiv Detail & Related papers (2021-10-15T21:46:39Z) - Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn
Chatbot Responding with Intention [55.77218465471519]
This paper proposes an innovative framework to train chatbots to possess human-like intentions.
Our framework included a guiding robot and an interlocutor model that plays the role of humans.
We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
arXiv Detail & Related papers (2021-03-30T15:24:37Z) - Evaluating Empathetic Chatbots in Customer Service Settings [6.523873187705393]
We show that a blended skills chatbots model that responds to customer queries is more likely to resemble actual human agent response if it is trained to recognize emotion and exhibit appropriate empathy.
For our analysis, we leverage a Twitter customer service dataset containing several million customer->agent dialog examples in customer service contexts from 20 well-known brands.
arXiv Detail & Related papers (2021-01-05T03:34:35Z) - Learning Improvised Chatbots from Adversarial Modifications of Natural
Language Feedback [19.026954124876582]
We propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation.
The generator's goal is to convert the feedback into a response that answers the user's previous utterance and to fool the discriminator.
arXiv Detail & Related papers (2020-10-14T17:33:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.