no code implementations • EMNLP (newsum) 2021 • Haoran Li, Arash Einolghozati, Srinivasan Iyer, Bhargavi Paranjape, Yashar Mehdad, Sonal Gupta, Marjan Ghazvininejad
To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary.
no code implementations • SIGDIAL (ACL) 2021 • Peyman Heidari, Arash Einolghozati, Shashank Jain, Soumya Batra, Lee Callender, Ankit Arun, Shawn Mei, Sonal Gupta, Pinar Donmez, Vikas Bhardwaj, Anuj Kumar, Michael White
In this paper, we study the utilization of pre-trained language models to enable few-shotNatural Language Generation (NLG) in task-oriented dialog systems.
no code implementations • ACL (WebNLG, INLG) 2020 • Zixiaofan Yang, Arash Einolghozati, Hakan Inan, Keith Diedrick, Angela Fan, Pinar Donmez, Sonal Gupta
Converting a knowledge graph or sub-graph to natural text is useful when answering questions based on a knowledge base.
no code implementations • 28 Feb 2024 • Sahithya Ravi, Patrick Huber, Akshat Shrivastava, Aditya Sagar, Ahmed Aly, Vered Shwartz, Arash Einolghozati
The emergence of Large Language Models (LLMs) has brought to light promising language generation capabilities, particularly in performing tasks like complex reasoning and creative writing.
no code implementations • 19 Dec 2022 • Asish Ghoshal, Arash Einolghozati, Ankit Arun, Haoran Li, Lili Yu, Vera Gor, Yashar Mehdad, Scott Wen-tau Yih, Asli Celikyilmaz
Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries.
no code implementations • 4 Oct 2022 • Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari
Driven by this question, we leverage an indexing-efficient dense retriever (i. e. DrBoost) and introduce a LITE retriever that further reduces the memory of DrBoost.
no code implementations • 14 May 2021 • Haoran Li, Arash Einolghozati, Srinivasan Iyer, Bhargavi Paranjape, Yashar Mehdad, Sonal Gupta, Marjan Ghazvininejad
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability.
no code implementations • EACL 2021 • Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, Sonal Gupta
Being able to parse code-switched (CS) utterances, such as Spanish+English or Hindi+English, is essential to democratize task-oriented semantic parsing systems for certain locales.
1 code implementation • EMNLP 2020 • Arash Einolghozati, Anchit Gupta, Keith Diedrick, Sonal Gupta
We introduce a new task of rephrasing for a more natural virtual assistant.
1 code implementation • 30 Dec 2019 • Varun Gangal, Abhinav Arora, Arash Einolghozati, Sonal Gupta
We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.
no code implementations • 12 Nov 2019 • Arash Einolghozati, Sonal Gupta, Mrinal Mohit, Rushin Shah
However, evaluating a model's robustness to these changes is harder for language since words are discrete and an automated change (e. g. adding `noise') to a query sometimes changes the meaning and thus labels of a query.
no code implementations • 15 Feb 2019 • Arash Einolghozati, Panupong Pasupat, Sonal Gupta, Rushin Shah, Mrinal Mohit, Mike Lewis, Luke Zettlemoyer
Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [Gupta et al 2018].
no code implementations • 12 Apr 2018 • Colin Lockard, Xin Luna Dong, Arash Einolghozati, Prashant Shiralkar
In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision.
1 code implementation • 17 Feb 2017 • Parminder Bhatia, Marsal Gavalda, Arash Einolghozati
While liking or upvoting a post on a mobile app is easy to do, replying with a written note is much more difficult, due to both the cognitive load of coming up with a meaningful response as well as the mechanics of entering the text.