mlsanityData to 14 Oct 2026

Natural Language Sentence Generation from API Specifications

arXiv 2206.06868 ↗First posted 1 Jun 2022cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 46 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

28 of 46 references.

References · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  6. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  7. 2018Deep contextualized word representationscited by 2,290
  8. 2018Hierarchical Neural Story Generationcited by 1,193
  9. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  10. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  11. 2018Universal Sentence Encodercited by 489
  12. 2020Efficient Intent Detection with Dual Sentence Encoderscited by 433
16 more
  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  2. 2019PAWS: Paraphrase Adversaries from Word Scramblingcited by 402
  3. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 386
  4. 2019Not Enough Data? Deep Learning to the Rescue!cited by 175
  5. 2019Benchmarking Natural Language Understanding Services for building Conversational Agentscited by 166
  6. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  7. 2020Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Trackingcited by 65
  8. 2020Generative Data Augmentation for Commonsense Reasoningcited by 49
  9. 2019Data Augmentation via Dependency Tree Morphing for Low-Resource Languagescited by 45
  10. 2017ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translationscited by 44
  11. 2020Paraphrase Augmented Task-Oriented Dialog Generationcited by 43
  12. 2021MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understandingcited by 36
  13. 2020A Conversational Digital Assistant for Intelligent Process Automationcited by 8
  14. 2020Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluationscited by 8
  15. 2018Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotationscited by 2
  16. 2018Machine Readable Web APIs with Schema.org Action Annotationscited by 1

Built on by

Not cited yet.