Sina Semnani
15 papers · 2020–2025 · 4 conferences · across top CS/AI conferences
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ACL (4)
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Keywords
large language model
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few-shot learning
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semantic parsing
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zero-shot learning
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question answering
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dialogue system
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factual accuracy
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language model
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semantic parser
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fact verification
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retrieval-augmented generation
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task-oriented dialogue
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knowledge graph
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information retrieval
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knowledge base
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neural machine translation
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machine translation
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dialogue state tracking
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Papers
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World
ACL 2025
Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models
EMNLP 2025
CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
EMNLP 2025
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
EMNLP 2024
SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing
ACL 2024
SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models
NAACL 2024
Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations
EMNLP 2024
Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval
EMNLP 2024
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata
EMNLP 2023
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
ACL 2023
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation
EACL 2023
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia
EMNLP 2023
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation
ACL 2022
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
EMNLP 2020
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
EMNLP 2020