activity
20192023
most citedWikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia

32 citations · 111 across the 16 of their papers we have counts for

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Showing 2020 · cs.CLShow all

5 papers · 2 filters

cs.CL2020

Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation

Mehrad Moradshahi, Giovanni Campagna, Sina J. Semnani +2

We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is t…

cs.CL2020★ 4 cited

AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data

Silei Xu, Sina J. Semnani, Giovanni Campagna +1

We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA au…

cs.CL2020★ 1 cited

A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation

Giovanni Campagna, Sina J. Semnani, Ryan Kearns +3

Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ) conversations suffer from the difficulty in acquiring a high-quality, manually annotated training set.…

cs.CL2020★ 1 cited

Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi +1

Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new…

cs.CL2020★ 9 cited

Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web

Silei Xu, Giovanni Campagna, Jian Li +1

Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can g…