Search Results for author:
Found 34 papers, 11 papers with code
EASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle
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.
Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task
Converting a knowledge graph or sub-graph to natural text is useful when answering questions based on a knowledge base.
Getting to Production with Few-shot Natural Language Generation Models
In this paper, we study the utilization of pre-trained language models to enable few-shotNatural Language Generation (NLG) in task-oriented dialog systems.
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression
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.
Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation
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.
Ranked #20 on
Motion Synthesis
on HumanML3D
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation
In contrast, we propose a parameter-free temporal shift module that can leverage the spatial U-Net as is for video generation.
Text-Conditional Contextualized Avatars For Zero-Shot Personalization
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.
SpaText: Spatio-Textual Representation for Controllable Image Generation
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
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).
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
(CLIP-FID metric)
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training
Consequently, we propose a novel QA dataset based on the Common Crawl project in this paper.
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
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.
Ranked #2 on
Passage Retrieval
on EntityQuestions
Domain-matched Pre-training Tasks for Dense Retrieval
Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks.
Ranked #2 on
Passage Retrieval
on Natural Questions
(using extra training data)
EASE: Extractive-Abstractive Summarization with Explanations
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability.
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing
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
We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning.
Ranked #3 on
Text Summarization
on GigaWord
(using extra training data)
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing
Training with soft targets instead of hard targets has been shown to improve performance and calibration of deep neural networks.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases.
Ranked #1 on
Open-Domain Question Answering
on WebQuestions
(using extra training data)
Knowledge Base Question Answering
Open-Domain Question Answering
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
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)
Sound Natural: Content Rephrasing in Dialog Systems
We introduce a new task of rephrasing for a more natural virtual assistant.
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing
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.).
Conversational Semantic Parsing
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.
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
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.
Better Fine-Tuning by Reducing Representational Collapse
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
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog
We are hitherto the first to investigate the use of generative classifiers for OOD detection at test-time.
Improving Robustness of Task Oriented Dialog Systems
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.
Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog
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.
Improving Semantic Parsing for Task Oriented Dialog
Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [Gupta et al 2018].
PyText: A Seamless Path from NLP research to production
We introduce PyText - a deep learning based NLP modeling framework built on PyTorch.
Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog
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.
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations
Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots.
