no code implementations • 22 Feb 2024 • Yijia Shao, Yucheng Jiang, Theodore A. Kanell, Peter Xu, Omar Khattab, Monica S. Lam
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages.
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.
no code implementations • 16 Jun 2023 • Jackie Junrui Yang, Yingtian Shi, Yuhan Zhang, Karina Li, Daniel Wan Rosli, Anisha Jain, Shuning Zhang, Tianshi Li, James A. Landay, Monica S. Lam
This paper targets complex interactions, where users can issue multimodal commands that translate into one of the possible exponential combinations of actions/function invocations.
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 • 4 Nov 2021 • Mehrad Moradshahi, Victoria Tsai, Giovanni Campagna, Monica S. Lam
On RiSAWOZ, CrossWOZ, CrossWOZ-EN, and MultiWOZ-ZH datasets we improve the state of the art by 11%, 17%, 20%, and 0. 3% in joint goal accuracy.
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.
1 code implementation • ACL 2020 • Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, Monica S. Lam
We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2. 1 dataset.
3 code implementations • 16 Jan 2020 • Silei Xu, Giovanni Campagna, Jian Li, Monica S. Lam
The key concept is to cover the space of possible compound queries on the database with a large number of in-domain questions synthesized with the help of a corpus of generic query templates.
no code implementations • 14 Jan 2020 • Michael H. Fischer, Richard R. Yang, Monica S. Lam
This paper presents ImagineNet, a tool that uses a novel neural style transfer model to enable end-users and app developers to restyle GUIs using an image of their choice.
1 code implementation • 25 Oct 2019 • Mehrad Moradshahi, Hamid Palangi, Monica S. Lam, Paul Smolensky, Jianfeng Gao
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.
1 code implementation • 18 Apr 2019 • Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, Monica S. Lam
We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code.