32 citations · 111 across the 16 of their papers we have counts for
12 papers · 1 filter
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
Mehrad Moradshahi, Tianhao Shen, Kalika Bali +16
Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost…
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia
Sina J. Semnani, Violet Z. Yao, Heidi C. Zhang +1
This paper presents the first few-shot LLM-based chatbot that almost never hallucinates and has high conversationality and low latency. WikiChat is grounded on the English Wikipedi…
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation
Mehrad Moradshahi, Sina J. Semnani, Monica S. Lam
Task-oriented Dialogue (ToD) agents are mostly limited to a few widely-spoken languages, mainly due to the high cost of acquiring training data for each language. Existing low-cost…
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues
Mehrad Moradshahi, Victoria Tsai, Giovanni Campagna +1
Robust state tracking for task-oriented dialogue systems currently remains restricted to a few popular languages. This paper shows that given a large-scale dialogue data set in one…
Grounding Open-Domain Instructions to Automate Web Support Tasks
Nancy Xu, Sam Masling, Michael Du +4
Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation. We introduce a task and dataset to train AI agents from…
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…
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…
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.…
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…
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…
HUBERT Untangles BERT to Improve Transfer across NLP Tasks
Mehrad Moradshahi, Hamid Palangi, Monica S. Lam +2
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.…
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
Giovanni Campagna, Silei Xu, Mehrad Moradshahi +2
To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology…