Papers by Giovanni Campagna
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing techniques for zero-shot transfer learning for multi-domain dialogue state tracking are expensive and require human errors, delays in annotation, and normalization issues. |
| Approach: | They propose a zero-shot transfer learning technique where training data are synthesized from an abstract dialogue model and the ontology of the domain. |
| Outcome: | The proposed technique improves the state of the art on the multi-domain dialogue state tracking dataset by 21%. |
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation (2022.findings-acl)
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| Challenge: | Existing approaches to build effective semantic parsers for Wizard-of-Oz are insufficient. |
| Approach: | They propose a new dialogue representation and a sample-efficient methodology that can predict precise dialogue states in WOZ conversations. |
| Outcome: | The proposed model can predict precise dialogue states in WOZ conversations. |
Grounding Open-Domain Instructions to Automate Web Support Tasks (2021.naacl-main)
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| Challenge: | RUSS is a task and dataset to ground natural language instructions on the web to perform previously unseen tasks. |
| Approach: | They build a task and dataset to ground AI agents from open-domain, step-by-step instructions on the web. |
| Outcome: | The proposed model outperforms existing models that map instructions to actions without WebLang. |
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
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| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions (2023.findings-emnlp)
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| Challenge: | Existing approaches to text classification require large annotated corpora to train or long context to fit many examples. |
| Approach: | They propose a method to few-shot text classification using an LLM. |
| Outcome: | The proposed approach yields high accuracy classifiers within 79% of the performance of models trained with larger datasets while using only 1% of their training sets. |
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation (2020.emnlp-main)
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| Challenge: | a new toolkit for localizing a semantic parser for a language is proposed . the proposed approach is based on a method for question answering systems . |
| Approach: | They propose a toolkit that leverages Neural Machine Translation systems to localize a semantic parser for a new language. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in 10 new languages . it can be deployed in restaurants and hotels in less than 24 hours . |
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues (2023.eacl-main)
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| Challenge: | Existing methods for predicting state of a conversation are limited to a few languages . a method that can be applied to other languages will benefit the large population of speakers of many other languages. |
| Approach: | They propose to automatically translate large-scale dialogue data sets in one language to produce an effective semantic parser for other languages using machine translation. |
| Outcome: | The proposed model reduces the compounding effect of translation errors without harming the accuracy in practice. |