Search Results for author:
Found 36 papers, 20 papers with code
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality
Along with this, we propose novel negative mining techniques in the scene graph space for improving attribute binding and relation understanding.
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
Multi-Head State Space Model for Speech Recognition
State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches.
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.
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.
CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding
Whether heuristic or learned, these methods ignore instance-level attributes of objects such as color and style, and as a result may produce visually less coherent scenes.
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search.
Adapting Pretrained Text-to-Text Models for Long Text Sequences
We present an empirical study of adapting an existing pretrained text-to-text model for long-sequence inputs.
Ranked #1 on
Text Summarization
on QMSum
SCROLLS: Standardized CompaRison Over Long Language Sequences
NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild.
Ranked #8 on
Long-range modeling
on SCROLLS
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.
Simple Local Attentions Remain Competitive for Long-Context Tasks
Many NLP tasks require processing long contexts beyond the length limit of pretrained models.
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation
Our experiments show that there is a trade-off between understanding tasks and generation tasks while using the same model, and a feasible way to improve both tasks is to use more data.
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
In this work, we present such a system for the emergent domain of COVID-19.
Zero-shot Fact Verification by Claim Generation
However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive.
Unsupervised Multi-hop Question Answering by Question Generation
Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive.
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 #15 on
Question Answering
on HotpotQA
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering
In this work, we propose a simple and resource-efficient method to pretrain the paragraph encoder.
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
3) a hybrid model that combines heterogeneous information to find the answer.
Ranked #4 on
Question Answering
on HybridQA
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i. e., the question-answer pairs.
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model
Models trained with our new objective yield significant improvements on the fact completion task.
Do Multi-hop Readers Dream of Reasoning Chains?
General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i. e. the ability to reason with information collected from multiple passages to derive the answer.
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i. e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model.
Neural Correction Model for Open-Domain Named Entity Recognition
In this work, to address the low precision and recall problems, we first utilize DBpedia as the source of distant supervision to annotate abstracts from Wikipedia and design a neural correction model trained with a human-annotated NER dataset, DocRED, to correct the false entity labels.
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification
Many tasks in natural language processing can be viewed as multi-label classification problems.
Meta Reasoning over Knowledge Graphs
The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.
TWEETQA: A Social Media Focused Question Answering Dataset
With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge.
Self-Supervised Learning for Contextualized Extractive Summarization
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.
Sentence Embedding Alignment for Lifelong Relation Extraction
We formulate such a challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks.
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.
Ranked #3 on
Entity Typing
on Ontonotes v5 (English)
SafeRoute: Learning to Navigate Streets Safely in an Urban Environment
We propose SafeRoute, a novel solution to the problem of navigating cities and avoiding street harassment and crime.
One-Shot Relational Learning for Knowledge Graphs
Knowledge graphs (KGs) are the key components of various natural language processing applications.
Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents
We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs.
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task.
Model-based Reinforcement Learning
reinforcement-learning
+4
Variational Knowledge Graph Reasoning
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community.
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
We study the problem of learning to reason in large scale knowledge graphs (KGs).
Ranked #1 on
Link Prediction
on NELL-995
(Mean AP metric)
