Natural Language Sentence Generation from API Specifications

• URL: http://arxiv.org/abs/2206.06868v1
• Date: Wed, 1 Jun 2022 15:50:14 GMT
• Title: Natural Language Sentence Generation from API Specifications
• Authors: Siyu Huo, Kushal Mukherjee, Jayachandu Bandlamudi, Vatche Isahagian, Vinod Muthusamy and Yara Rizk
• Abstract summary: We propose a system to generate sentences to train intent recognition models. The human-in-the-loop interaction will provide further improvement on the system.
• Score: 5.192671914929481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks. Unfortunately, they may not be accessible to the business users who need them but are not equipped with the necessary technical skills to leverage them. Wrapping these APIs with chatbot capabilities is one solution to make these automation solutions interactive. In this work, we propose a system to generate sentences to train intent recognition models, a crucial component within chatbots to understand natural language utterances from users. Evaluation of our approach based on deep learning models showed promising and inspiring results, and the human-in-the-loop interaction will provide further improvement on the system.

Related papers

• Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition [110.8431434620642]
  We introduce the generative speech transcription error correction (GenSEC) challenge. This challenge comprises three post-ASR language modeling tasks: (i) post-ASR transcription correction, (ii) speaker tagging, and (iii) emotion recognition. We discuss insights from baseline evaluations, as well as lessons learned for designing future evaluations.
  arXiv  Detail & Related papers  (2024-09-15T16:32:49Z)
• Interpreting and learning voice commands with a Large Language Model for a robot system [0.0]
  The use of Large Language Models (LLMs) like GPT-4 has enhanced robot capabilities, allowing for real-time interaction and decision-making. This project focuses on merging LLMs with databases to improve decision-making and enable knowledge acquisition for request interpretation problems.
  arXiv  Detail & Related papers  (2024-07-31T10:30:31Z)
• Automatically generating decision-support chatbots based on DMN models [0.0]
  We propose an approach for the automatic generation of fully functional, ready-to-use decisions-support chatbots based on a DNM decision model. With the aim of reducing chatbots development time and to allowing non-technical users the possibility of developing chatbots specific to their domain, all necessary phases were implemented in the Demabot tool.
  arXiv  Detail & Related papers  (2024-05-15T18:13:09Z)
• Creation Of A ChatBot Based On Natural Language Proccesing For Whatsapp [0.0]
  The objective of this study is to develop a chatbots based on natural language processing to improve customer satisfaction and improve the quality of service provided by the company through WhatsApp. The results of this study will provide a solid foundation for the design and development of effective chatbots for customer service.
  arXiv  Detail & Related papers  (2023-10-10T18:54:15Z)
• AI Based Chatbot: An Approach of Utilizing On Customer Service Assistance [0.0]
  The project aims to develop the system that could comply with complex questions and logical output answers. The ultimate goal is to give high-quality results (answers) based on user input (question)
  arXiv  Detail & Related papers  (2022-06-18T00:59:10Z)
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances [119.29555551279155]
  Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions.
  arXiv  Detail & Related papers  (2022-04-04T17:57:11Z)
• 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)
• Few-Shot Bot: Prompt-Based Learning for Dialogue Systems [58.27337673451943]
  Learning to converse using only a few examples is a great challenge in conversational AI. The current best conversational models are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL) We propose prompt-based few-shot learning which does not require gradient-based fine-tuning but instead uses a few examples as the only source of learning.
  arXiv  Detail & Related papers  (2021-10-15T14:36:45Z)
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation [80.29069988090912]
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. We propose to leverage offline robot datasets with crowd-sourced natural language labels. We find that our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%.
  arXiv  Detail & Related papers  (2021-09-02T17:42:13Z)
• Learning Adaptive Language Interfaces through Decomposition [89.21937539950966]
  We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition. Users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps.
  arXiv  Detail & Related papers  (2020-10-11T08:27:07Z)
• Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network [0.0]
  Natural Language Understanding (NLU) has been impressively improved by deep learning methods. This research focuses on Named Entity Recognition (NER) models which can be integrated into NLU service of a dataset. The NER model in the proposed architecture is based on artificial neural network which is trained on manually created entities.
  arXiv  Detail & Related papers  (2020-06-19T14:47:21Z)

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.