315 citations · 640 across the 17 of their papers we have counts for
17 papers
Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation
Samaneh Azadi, Akbar Shah, Thomas Hayes +2
Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models…
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation
Jie An, Songyang Zhang, Harry Yang +4
We propose Latent-Shift -- an efficient text-to-video generation method based on a pretrained text-to-image generation model that consists of an autoencoder and a U-Net diffusion m…
Text-Conditional Contextualized Avatars For Zero-Shot Personalization
Samaneh Azadi, Thomas Hayes, Akbar Shah +3
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 t…
SpaText: Spatio-Textual Representation for Controllable Image Generation
Omri Avrahami, Thomas Hayes, Oran Gafni +6
Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/…
Make-A-Video: Text-to-Video Generation without Text-Video Data
Uriel Singer, Adam Polyak, Thomas Hayes +10
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). Our intuition is simple: le…
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training
Patrick Huber, Armen Aghajanyan, Barlas Oğuz +4
With the rise of large-scale pre-trained language models, open-domain question-answering (ODQA) has become an important research topic in NLP. Based on the popular pre-training fin…
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
Xilun Chen, Kushal Lakhotia, Barlas Oğuz +6
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…
Domain-matched Pre-training Tasks for Dense Retrieval
Barlas Oğuz, Kushal Lakhotia, Anchit Gupta +8
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. A notable exception is information ret…
EASE: Extractive-Abstractive Summarization with Explanations
Haoran Li, Arash Einolghozati, Srinivasan Iyer +4
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. To achieve…
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing
Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda +2
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.…
Muppet: Massive Multi-task Representations with Pre-Finetuning
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava +3
We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
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 no…
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
Barlas Oguz, Xilun Chen, Vladimir Karpukhin +6
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior…
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Haoran Li, Abhinav Arora, Shuohui Chen +3
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. Available datasets su…
Improving Robustness of Task Oriented Dialog Systems
Arash Einolghozati, Sonal Gupta, Mrinal Mohit +1
Task oriented language understanding in dialog systems is often modeled using intents (task of a query) and slots (parameters for that task). Intent detection and slot tagging are,…
PyText: A Seamless Path from NLP research to production
Ahmed Aly, Kushal Lakhotia, Shicong Zhao +7
We introduce PyText - a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of se…