no code implementations • 1 Jun 2024 • Heidi C. Zhang, Sina J. Semnani, Farhad Ghassemi, Jialiang Xu, Shicheng Liu, Monica S. Lam
We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes, a hybrid question-answering (QA) pipeline that utilizes information from heterogeneous knowledge sources, including knowledge base, text, tables, and infoboxes.
no code implementations • 28 May 2024 • Andrew H. Lee, Sina J. Semnani, Galo Castillo-López, Gäel de Chalendar, Monojit Choudhury, Ashna Dua, Kapil Rajesh Kavitha, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Alexis Lombard, Mehrad Moradshahi, Gihyun Park, Nasredine Semmar, Jiwon Seo, Tianhao Shen, Manish Shrivastava, Deyi Xiong, Monica S. Lam
However, after manual evaluation of the validation set, we find that by correcting gold label errors and improving dataset annotation schema, GPT-4 with our prompts can achieve (1) 89. 6%-96. 8% accuracy in DST, and (2) more than 99% correct response generation across different languages.
1 code implementation • 16 Nov 2023 • Shicheng Liu, Jialiang Xu, Wesley Tjangnaka, Sina J. Semnani, Chen Jie Yu, Monica S. Lam
This paper presents the first conversational agent that supports the full generality of hybrid data access for large knowledge corpora, through a language we developed called SUQL (Structured and Unstructured Query Language).
1 code implementation • 30 Jun 2023 • Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gaël de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina J. Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica S. Lam
We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.
1 code implementation • 23 May 2023 • Sina J. Semnani, Violet Z. Yao, Heidi C. Zhang, Monica S. Lam
WikiChat generates a response from an LLM, retains only the grounded facts, and combines them with additional information it retrieves from the corpus to form factual and engaging responses.
1 code implementation • 23 May 2023 • Silei Xu, Shicheng Liu, Theo Culhane, Elizaveta Pertseva, Meng-Hsi Wu, Sina J. Semnani, Monica S. Lam
By pairing our semantic parser with GPT-3, we combine verifiable results with qualified GPT-3 guesses to provide useful answers to 96% of the questions in dev.
1 code implementation • 18 Feb 2023 • Mehrad Moradshahi, Sina J. Semnani, Monica S. Lam
We propose automatic methods that use ToD training data in a source language to build a high-quality functioning dialogue agent in another target language that has no training data (i. e. zero-shot) or a small training set (i. e. few-shot).
1 code implementation • 23 Mar 2022 • Monica S. Lam, Giovanni Campagna, Mehrad Moradshahi, Sina J. Semnani, Silei Xu
Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations.
1 code implementation • EMNLP 2020 • Mehrad Moradshahi, Giovanni Campagna, Sina J. Semnani, Silei Xu, Monica S. Lam
We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.
3 code implementations • EMNLP 2020 • Silei Xu, Sina J. Semnani, Giovanni Campagna, Monica S. Lam
To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset.
1 code implementation • Findings (ACL) 2022 • Giovanni Campagna, Sina J. Semnani, Ryan Kearns, Lucas Jun Koba Sato, Silei Xu, Monica S. Lam
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.
no code implementations • 2 Sep 2020 • Sina J. Semnani, Manish Pandey
Open-domain question answering (QA) is the tasl of identifying answers to natural questions from a large corpus of documents.