Building Natural Language Interfaces to Web APIs

By Yu Su, Ahmed Hassan Awadallah, Madian Khabsa, Patrick Pantel, Michael Gamon, Mark J. Encarnación. 2017

64 citations recorded by the source corpus.

Subfields: Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Natural Language Processing

Citations in the corpus

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents (2022, background): “Some benchmarks only contain either a single classification task [39, 46, 31] or interactions containing only a handful of different pages in each episode [43].”
User Utterance Acquisition for Training Task-Oriented Bots: A Review of Challenges, Techniques and Opportunities (2020, methodology): “, hierarchical probabilistic model.(4)) Adaptive termination strategy is also a similar approachbasedon theCoverage metric.”
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020, methodology): “Various semantic parsing datasets have been generated with this technique (Su et al., 2017; Zhong et al., 2017) and the technique has also been adapted to the multiturn setting (Cheng et al., 2018; Shah et al., 2018).”
Mapping natural language commands to web elements (2018, background): “Examples of such actions include API calls (Young et al., 2013; Su et al., 2017; Bordes and Weston, 2017), database queries (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2007; Berant et al.”
Mind2Web: Towards a Generalist Agent for the Web (2023, methodology): “For example, it may be used as a plugin for ChatGPT [20] to directly acquire information and carry out actions on HTML websites, instead of only retrieving web content through a retriever tool [5, 14] or relying on pre-defined APIs for each web service [30, 33].”
Natural Language Interfaces with Fine-Grained User Interaction: A Case Study on Web APIs (2018, methodology): “More specically, we will follow the seing dened by Su et al.[27] and focus on web APIs that follow the REST architectural style [10], i.”
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback (2020, background): “(2018) make a similar observation about parsing text to API calls (Su et al., 2017) and show that parsing mistakes could be easily corrected if humans are afforded a means of providing precise feedback.”
A Survey of Human-in-the-loop for Machine Learning (2021, background): “Since then, human feedback has been demonstrated as a crucial contribution of semantic parsing [84, 85, 86].”
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study (2019, background): “Natural language interfaces that allow users to query data and invoke services without programming have been identiﬁed as a key application of semantic parsing (Berant et al., 2013; Thomason et al., 2015; Dong and Lapata, 2016; Zhong et al., 2017; Campagna et al., 2017; Su et al., 2017).”
An Application-Independent Approach to Building Task-Oriented Chatbots with Interactive Continual Learning (2020, background): “[37, 30] proposed end-to-end approaches for building NLI for web APIs.”
FLIN: A Flexible Natural Language Interface for Web Navigation (2020, unknown): “…includes learning tasks from demonstrations (Allen et al., 2007), building reinforcement learning agents (Shi et al., 2017b; Liu et al., 2018b), semantic parsing using seq2seq models to map NL commands into web APIs (Su et al., 2017, 2018) and generating task ﬂows from APIs (Williams et al., 2019).”