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Found 32 papers, 20 papers with code
Infosys Machine Translation System for WMT20 Similar Language Translation Task
This paper describes Infosys’s submission to the WMT20 Similar Language Translation shared task.
SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language Specifications
Evaluations show that even when using a state-of-the-art language model, there is significant room for improvement, with the best models achieving an F1 score of only 60. 5 and 33. 3 in the named-entity-recognition and dependency-link-prediction sub-tasks, respectively.
Generative Representational Instruction Tuning
Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss.
Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language
We propose LENS, a modular approach for tackling computer vision problems by leveraging the power of large language models (LLMs).
OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks.
The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific document representations.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs
We implement our technique in a novel system, FeedLens, which is built over Semantic Scholar, a production system for navigating the scientific literature KG.
Embedding Recycling for Language Models
Real-world applications of neural language models often involve running many different models over the same corpus.
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality
We present a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning, which we call Winoground.
Ranked #41 on
Visual Reasoning
on Winoground
FLAVA: A Foundational Language And Vision Alignment Model
State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks.
Ranked #4 on
Image Retrieval
on MS COCO
Human-Adversarial Visual Question Answering
Human subjects interact with a state-of-the-art VQA model, and for each image in the dataset, attempt to find a question where the model's predicted answer is incorrect.
TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text
A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
Dynabench: Rethinking Benchmarking in NLP
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking.
Physics Informed Convex Artificial Neural Networks (PICANNs) for Optimal Transport based Density Estimation
Optimal Mass Transport (OMT) is a well studied problem with a variety of applications in a diverse set of fields ranging from Physics to Computer Vision and in particular Statistics and Data Science.
UniT: Multimodal Multitask Learning with a Unified Transformer
We propose UniT, a Unified Transformer model to simultaneously learn the most prominent tasks across different domains, ranging from object detection to natural language understanding and multimodal reasoning.
Open4Business(O4B): An Open Access Dataset for Summarizing Business Documents
The dataset introduces a new challenge for summarization in the business domain, requiring highly abstractive and more concise summaries as compared to other existing datasets.
Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation
We also demonstrate that reducing the task of room navigation to point navigation improves the performance further.
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes.
Are we pretraining it right? Digging deeper into visio-linguistic pretraining
Surprisingly, we show that automatically generated data in a domain closer to the downstream task (e. g., VQA v2) is a better choice for pretraining than "natural" data but of a slightly different domain (e. g., Conceptual Captions).
TextCaps: a Dataset for Image Captioning with Reading Comprehension
Image descriptions can help visually impaired people to quickly understand the image content.
Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA
Recent work has explored the TextVQA task that requires reading and understanding text in images to answer a question.
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks.
Modeling the Long Term Future in Model-Based Reinforcement Learning
This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration.
Towards VQA Models That Can Read
We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset.
Ranked #3 on
Visual Question Answering (VQA)
on VizWiz 2018
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration.
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Learning when to communicate and doing that effectively is essential in multi-agent tasks.
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas
We propose a new recurrent generative model for generating images from text captions while attending on specific parts of text captions.
Neural Network Acceptability Judgments
This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset.
Ranked #46 on
Natural Language Inference
on MultiNLI
Natural Language Inference
Natural Language Understanding
+2
