Search Results for author: Sonal Gupta

Found 34 papers, 11 papers with code

Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression

no code implementations17 Nov 2023 Animesh Sinha, Bo Sun, Anmol Kalia, Arantxa Casanova, Elliot Blanchard, David Yan, Winnie Zhang, Tony Nelli, Jiahui Chen, Hardik Shah, Licheng Yu, Mitesh Kumar Singh, Ankit Ramchandani, Maziar Sanjabi, Sonal Gupta, Amy Bearman, Dhruv Mahajan

Evaluation results show our method improves visual quality by 14%, prompt alignment by 16. 2% and scene diversity by 15. 3%, compared to prompt engineering the base Emu model for stickers generation.

Image Generation Prompt Engineering

Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation

no code implementations ICCV 2023 Samaneh Azadi, Akbar Shah, Thomas Hayes, Devi Parikh, Sonal Gupta

However, existing approaches are limited by their reliance on relatively small-scale motion capture data, leading to poor performance on more diverse, in-the-wild prompts.

Motion Synthesis Text-to-Video Generation +1

Text-Conditional Contextualized Avatars For Zero-Shot Personalization

no code implementations14 Apr 2023 Samaneh Azadi, Thomas Hayes, Akbar Shah, Guan Pang, Devi Parikh, Sonal Gupta

Recent large-scale text-to-image generation models have made significant improvements in the quality, realism, and diversity of the synthesized images and enable users to control the created content through language.

Text to 3D

SpaText: Spatio-Textual Representation for Controllable Image Generation

no code implementations CVPR 2023 Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischinski, Ohad Fried, Xi Yin

Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based.

Make-A-Video: Text-to-Video Generation without Text-Video Data

2 code implementations29 Sep 2022 Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, Yaniv Taigman

We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V).

Image Generation Super-Resolution +2

Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?

2 code implementations13 Oct 2021 Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, Wen-tau Yih

Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare entities in the query and to generalize to out-of-domain data.

Open-Domain Question Answering Passage Retrieval +1

EASE: Extractive-Abstractive Summarization with Explanations

no code implementations14 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.

Abstractive Text Summarization Document Summarization +1

El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing

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.

Data Augmentation Semantic Parsing

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

2 code implementations ACL 2021 Armen Aghajanyan, Luke Zettlemoyer, Sonal Gupta

Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the low data regime.

 Ranked #1 on Transfer Learning on Amazon Review Polarity (Structure Aware Intrinsic Dimension metric)

Generalization Bounds Language Modelling +3

Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing

no code implementations EMNLP 2020 Xilun Chen, Asish Ghoshal, Yashar Mehdad, Luke Zettlemoyer, Sonal Gupta

Task-oriented semantic parsing is a critical component of virtual assistants, which is responsible for understanding the user's intents (set reminder, play music, etc.).

Domain Adaptation Meta-Learning +2

Conversational Semantic Parsing

no code implementations EMNLP 2020 Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Mike Haeger, Haoran Li, Yashar Mehdad, Ves Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta

In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session.

dialog state tracking Semantic Parsing

MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark

no code implementations EACL 2021 Haoran Li, Abhinav Arora, Shuohui Chen, Anchit Gupta, Sonal Gupta, Yashar Mehdad

Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets.

Benchmarking Semantic Parsing +1

Better Fine-Tuning by Reducing Representational Collapse

3 code implementations ICLR 2021 Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, Sonal Gupta

Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods.

Abstractive Text Summarization Cross-Lingual Natural Language Inference

Improving Robustness of Task Oriented Dialog Systems

no code implementations12 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.

Adversarial Attack Data Augmentation +4

Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog

no code implementations IJCNLP 2019 Panupong Pasupat, Sonal Gupta, M, Karishma yam, Rushin Shah, Mike Lewis, Luke Zettlemoyer

We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes.

Semantic Parsing valid

Improving Semantic Parsing for Task Oriented Dialog

no code implementations15 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].

Language Modelling Re-Ranking +1

Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog

no code implementations NAACL 2019 Sebastian Schuster, Sonal Gupta, Rushin Shah, Mike Lewis

We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations.

Cross-Lingual Transfer Machine Translation +1

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