Search Results for author:
Found 16 papers, 9 papers with code
MLQA: Evaluating Cross-lingual Extractive Question Answering
An alternative to building large monolingual training datasets is to develop cross-lingual systems which can transfer to a target language without requiring training data in that language.
Dense Passage Retrieval for Open-Domain Question Answering
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.
Ranked #1 on
Question Answering
on NaturalQA
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER.
Ranked #14 on
Question Answering
on HotpotQA
Joint Verification and Reranking for Open Fact Checking Over Tables
Structured information is an important knowledge source for automatic verification of factual claims.
Multi-task Retrieval for Knowledge-Intensive Tasks
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking.
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)
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
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.
Simple Local Attentions Remain Competitive for Long-Context Tasks
Many NLP tasks require processing long contexts beyond the length limit of pretrained models.
Boosted Dense Retriever
DrBoost is trained in stages: each component model is learned sequentially and specialized by focusing only on retrieval mistakes made by the current ensemble.
The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
In order to address increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web-scale knowledge, lack of structure, inconsistent quality and noise.
CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding
Whether heuristic or learned, these methods ignore instance-level visual attributes of objects, and as a result may produce visually less coherent scenes.
3DGen: Triplane Latent Diffusion for Textured Mesh Generation
We take another step along this direction, combining these developments in a two-step pipeline consisting of 1) a triplane VAE which can learn latent representations of textured meshes and 2) a conditional diffusion model which generates the triplane features.
Hierarchical Video-Moment Retrieval and Step-Captioning
Our hierarchical benchmark consists of video retrieval, moment retrieval, and two novel moment segmentation and step captioning tasks.
VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation
We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamental vision-language concepts, and then adapted to video data in an intermediate video-text pre-training stage to learn video-specific skills such as spatio-temporal reasoning.
Text-guided 3D Human Generation from 2D Collections
To address this \texttt{T3H} task, we propose Compositional Cross-modal Human (CCH).
Ranked #1 on
Text-to-3D-Human Generation
on SHHQ
