DataBloomDeutsche Bank Wednesday announced a partnership with NVIDIA to accelerate the use of AI and machine learning in the financial services sector. The announcement follows months of testing to explore use cases that could support the bank’s strategic ambitions to 2025 and beyond. “Accelerated computing and AI are at a tipping point, and we’re bringing Read article >
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From rapidly fluctuating demand to staffing shortages and supply chain complexity, enterprises have navigated numerous challenges the past few years. Many companies seeking strong starts to 2023 are planning to use AI and accelerated computing to drive growth while saving costs. To support these early adopters — as well as those just beginning their AI Read article >
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Time to tackle one of the most challenging tasks for aspiring movie makers — creating aesthetically pleasing visual effects — courtesy of visual effects artist and filmmaker Jay Lippman this week In the NVIDIA Studio.
The post Visual Effects Artist Jay Lippman Takes Viewers Behind the Camera This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
Join this webinar on December 13 and learn how to estimate obstacle distances with stereo cameras using bespoke, pre-trained DNN models ESS, and Bi3D.
Join this webinar on December 13 and learn how to estimate obstacle distances with stereo cameras using bespoke, pre-trained DNN models ESS, and Bi3D.
Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. With increasing focus on online privacy, there’s an opportunity to identify ML algorithms that have better privacy-utility trade-offs. Differential privacy (DP) has emerged as a popular framework for developing ML algorithms responsibly with provable privacy guarantees. It has been extensively studied in the privacy literature, deployed in industrial applications and employed by the U.S. Census. Intuitively, the DP framework enables ML models to learn population-wide properties, while protecting user-level information.
When training ML models, algorithms take a dataset as their input and produce a trained model as their output. Stochastic gradient descent (SGD) is a commonly used non-private training algorithm that computes the average gradient from a random subset of examples (called a mini-batch), and uses it to indicate the direction towards which the model should move to fit that mini-batch. The most widely used DP training algorithm in deep learning is an extension of SGD called DP stochastic gradient descent (DP-SGD).
DP-SGD includes two additional steps: 1) before averaging, the gradient of each example is norm-clipped if the L2 norm of the gradient exceeds a predefined threshold; and 2) Gaussian noise is added to the average gradient before updating the model. DP-SGD can be adapted to any existing deep learning pipeline with minimal changes by replacing the optimizer, such as SGD or Adam, with their DP variants. However, applying DP-SGD in practice could lead to a significant loss of model utility (i.e., accuracy) with large computational overheads. As a result, various research attempts to apply DP-SGD training on more practical, large-scale deep learning problems. Recent studies have also shown promising DP training results on computer vision and natural language processing problems.
In “Private Ad Modeling with DP-SGD”, we present a systematic study of DP-SGD training on ads modeling problems, which pose unique challenges compared to vision and language tasks. Ads datasets often have a high imbalance between data classes, and consist of categorical features with large numbers of unique values, leading to models that have large embedding layers and highly sparse gradient updates. With this study, we demonstrate that DP-SGD allows ad prediction models to be trained privately with a much smaller utility gap than previously expected, even in the high privacy regime. Moreover, we demonstrate that with proper implementation, the computation and memory overhead of DP-SGD training can be significantly reduced.
We evaluate private training using three ads prediction tasks: (1) predicting the click-through rate (pCTR) for an ad, (2) predicting the conversion rate (pCVR) for an ad after a click, and 3) predicting the expected number of conversions (pConvs) after an ad click. For pCTR, we use the Criteo dataset, which is a widely used public benchmark for pCTR models. We evaluate pCVR and pConvs using internal Google datasets. pCTR and pCVR are binary classification problems trained with the binary cross entropy loss and we report the test AUC loss (i.e., 1 – AUC). pConvs is a regression problem trained with Poisson log loss (PLL) and we report the test PLL.
For each task, we evaluate the privacy-utility trade-off of DP-SGD by the relative increase in the loss of privately trained models under various privacy budgets (i.e., privacy loss). The privacy budget is characterized by a scalar ε, where a lower ε indicates higher privacy. To measure the utility gap between private and non-private training, we compute the relative increase in loss compared to the non-private model (equivalent to ε = ∞). Our main observation is that on all three common ad prediction tasks, the relative loss increase could be made much smaller than previously expected, even for very high privacy (e.g., ε <= 1) regimes.
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| DP-SGD results on three ads prediction tasks. The relative increase in loss is computed against the non-private baseline (i.e., ε = ∞) model of each task. |
Privacy accounting estimates the privacy budget (ε) for a DP-SGD trained model, given the Gaussian noise multiplier and other training hyperparameters. Rényi Differential Privacy (RDP) accounting has been the most widely used approach in DP-SGD since the original paper. We explore the latest advances in accounting methods to provide tighter estimates. Specifically, we use connect-the-dots for accounting based on the privacy loss distribution (PLD). The following figure compares this improved accounting with the classical RDP accounting and demonstrates that PLD accounting improves the AUC on the pCTR dataset for all privacy budgets (ε).
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Batch size is a hyperparameter that affects different aspects of DP-SGD training. For instance, increasing the batch size could reduce the amount of noise added during training under the same privacy guarantee, which reduces the training variance. The batch size also affects the privacy guarantee via other parameters, such as the subsampling probability and training steps. There is no simple formula to quantify the impact of batch sizes. However, the relationship between batch size and the noise scale is quantified using privacy accounting, which calculates the required noise scale (measured in terms of the standard deviation) under a given privacy budget (ε) when using a particular batch size. The figure below plots such relations in two different scenarios. The first scenario uses fixed epochs, where we fix the number of passes over the training dataset. In this case, the number of training steps is reduced as the batch size increases, which could result in undertraining the model. The second, more straightforward scenario uses fixed training steps (fixed steps).
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| The relationship between batch size and noise scales. Privacy accounting requires a noise standard deviation, which decreases as the batch size increases, to meet a given privacy budget. As a result, by using much larger batch sizes than the non-private baseline (indicated by the vertical dotted line), the scale of Gaussian noise added by DP-SGD can be significantly reduced. |
In addition to allowing a smaller noise scale, larger batch sizes also allow us to use a larger threshold of norm clipping each per-example gradient as required by DP-SGD. Since the norm clipping step introduces biases in the average gradient estimation, this relaxation mitigates such biases. The table below compares the results on the Criteo dataset for pCTR with a standard batch size (1,024 examples) and a large batch size (16,384 examples), combined with large clipping and increased training epochs. We observe that large batch training significantly improves the model utility. Note that large clipping is only possible with large batch sizes. Large batch training was also found to be essential for DP-SGD training in Language and Computer Vision domains.
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| The effects of large batch training. For three different privacy budgets (ε), we observe that when training the pCTR models with large batch size (16,384), the AUC is significantly higher than with regular batch size (1,024). |
The per-example gradient norm calculation used for DP-SGD often causes computational and memory overhead. This calculation removes the efficiency of standard backpropagation on accelerators (like GPUs) that compute the average gradient for a batch without materializing each per-example gradient. However, for certain neural network layer types, an efficient gradient norm computation algorithm allows the per-example gradient norm to be computed without the need to materialize the per-example gradient vector. We also note that this algorithm can efficiently handle neural network models that rely on embedding layers and fully connected layers for solving ads prediction problems. Combining the two observations, we use this algorithm to implement a fast version of the DP-SGD algorithm. We show that Fast-DP-SGD on pCTR can handle a similar number of training examples and the same maximum batch size on a single GPU core as a non-private baseline.
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| The computation efficiency of our fast implementation (Fast-DP-SGD) on pCTR. |
Compared to the non-private baseline, the training throughput is similar, except with very small batch sizes. We also compare it with an implementation utilizing the JAX Just-in-Time (JIT) compilation, which is already much faster than vanilla DP-SGD implementations. Our implementation is not only faster, but it is also more memory efficient. The JIT-based implementation cannot handle batch sizes larger than 64, while our implementation can handle batch sizes up to 500,000. Memory efficiency is important for enabling large-batch training, which was shown above to be important for improving utility.
We have shown that it is possible to train private ads prediction models using DP-SGD that have a small utility gap compared to non-private baselines, with minimum overhead for both computation and memory consumption. We believe there is room for even further reduction of the utility gap through techniques such as pre-training. Please see the paper for full details of the experiments.
This work was carried out in collaboration with Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, and Avinash Varadarajan. We thank Silvano Bonacina and Samuel Ieong for many useful discussions.
The year 2022 has thus far been a momentous, thrilling, and an overwhelming year for AI aficionados. Get3D is pushing the boundaries of generative 3D modeling,…
The year 2022 has thus far been a momentous, thrilling, and an overwhelming year for AI aficionados. Get3D is pushing the boundaries of generative 3D modeling, an AI model can now diagnose breast cancer from MRIs as accurately as board-certified radiologists, and state-of-the-art speech AI models have widened their horizons to extended reality.
Pretrained models from NVIDIA have redefined performance this year, amused us on the stage of America’s Got Talent, won four global contests and a Best Inventions 2022 award from Time Magazine.
In addition to empowering researchers and data scientists, NVIDIA pretrained models are also empowering developers to create cutting-edge AI applications, by offering deep learning pretrained models and speedier convergence. To enable this, NVIDIA has spearheaded the research behind building and training these pretrained models for use cases like automatic speech recognition, pose estimation, object detection, 3D generation, semantic segmentation, and many more.
Model deployment can be streamlined, and users have already reaped the benefits over the last 3 months with 870 different NVIDIA pretrained models that support more than 50 use cases across several industries.
This post walks through a few of the top pretrained AI models that are behind groundbreaking AI applications.
NVIDIA NeMo is serving a variety of industries with cutting-edge AI application development for speech AI and natural language processing. The use cases include the creation of virtual assistants in Arabic and the facilitation of state-of-the-art automatic speech recognition (ASR) for financial audio.
For language-specific ASR, the NVIDIA NeMo deep learning conformer transducer pretrained model and conformer-ctc (connectionist temporal classification) pretrained model are well-liked. These models have high accuracy, a low word error rate, and a low character error rate due to their pretraining on a range of datasets, such as Librispeech and Mozilla Common Voice Data. They also have a robust AI architecture.
These models are laying the groundwork for state-of-the-art Kinyarwanda ASR model, Kabyle, Catalan, and many low-resource language pretrained models, which are bringing the usage of enhanced speech AI to low-resource languages, regions, and sectors.
For more information, see NeMo automatic speech recognition models.
To determine ‘who talked when,’ voice AI enthusiasts and application developers are fusing deep neural network speech recognition with speaker diarization architecture.
Beyond well-known uses like multi-speaker transcription in video conferencing, developers are gaining benefit from this AI architecture for special use cases:
Pretrained embeddings of the modified Emphasized Channel Attention, Propagation, and Aggregation in TDNN (ECAPA-TDNN) model are accessible with the NVIDIA NeMo toolkit. Fisher, Voxceleb, and real room-reaction data were used to train this deep neural network model for speaker identification and verification.
One of the best solutions for speaker diarization, ECAPA is based on the time-delay neural network (TDNN) and SE (squeeze and excite) structure with 22.3M parameters. It outperforms traditional TDNNs by emphasizing channel attention, propagation, and aggregation, as well as significantly reducing error rates.
For more information, see Speaker Diarization.
SegFormer is visionary research that uses AI to pioneer world-class image control. The original model and its variants are thriving in a variety of industries, including manufacturing, healthcare, automotive and retail. Its enormous potential is best demonstrated by applications like virtual changing rooms, robotic image control, medical imaging and diagnostics, and vision analytics in self-driving cars.
The semantic segmentation AI algorithm, a computer vision method for separating various objects in images, is the foundation of SegFormer. To increase performance to meet particular needs, the fine-tuned SegFormer is pretrained on datasets like ADE20k and CityScapes at several resolutions, such as 512×512, 640×640, 1024×1024, and so on. The AI design, which draws inspiration from the Transformer model architecture, produces cutting-edge outcomes in a variety of tasks.
For more information, see the NVlabs/SegFormer GitHub repo.
By detecting and identifying cars, people, road signs, and two-wheelers to comprehend traffic flow, TrafficCamNet has been driving smart city initiatives and detection technology for the automotive sector.
The model has been thoroughly trained using a vast amount of data that includes pictures of actual traffic crossings in US cities. The deep neural network model NVIDIA DetectNet_v2 detector is used with ResNet18 as a feature extractor. The AI architecture, which is sometimes referred to as GridBox object detection, employs bounding-box regression on a regular grid in the input image. The NVIDIA TAO toolbox can be used to access and further fine-tune the purpose-built, pretrained model TrafficCamNet for best-in-class accuracy.
For more information, see Purpose-Built Models.
NVIDIA pretrained models have won numerous awards for their cutting-edge performance, extraordinary research, and exemplary ability to solve real-world problems. Here are some notable wins.
Researchers from Argonne National Labs, NVIDIA, the Technical University of Munich, the University of Chicago, CalTech, Harvard University, and others developed one of the world’s largest genomics language models that predicts new COVID variants. For their work, they won the 2022 Gordon Bell Special Award.
The model informs timely public health intervention strategies and downstream vaccine development for emerging viral variants. The research was published in October 2022 and presents GenSLMs (genome-scale language models), which can accurately and rapidly identify variants of concern in the SARS-CoV-2 virus.
The large genomics language models were pretrained on >110M gene sequences and then a SARS-CoV-2 specific model was fine-tuned on 1.5M genomes with 2.5B and 25B trainable parameters, respectively. This research enables programmers to further genetic language modeling by creating applications that can assist different public health initiatives.
For more information, see Speaking the Language of the Genome: Gordon Bell Winner Applies Large Language Models to Predict New COVID Variants.
The Fully Attential Network (FAN) Transformer model from NVIDIA Research won the Robust Vision Challenge 2022. The team adopted the SegFormer head on top of an ImageNet-22k pretrained FAN-B-Hybrid model, as described in the Understanding The Robustness in Vision Transformers paper. The model was then further fine-tuned on a composed, large-scale dataset, similar to MSeg.
NVIDIA Research developed all the models used. The model achieved a state-of-the-art 87.1% accuracy and 35.8% mCE on ImageNet-1k and ImageNet-C with 76.8M parameters. We also demonstrated state-of-the-art accuracy and robustness in two downstream tasks, semantic segmentation and object detection.
For more information, see the NVlabs/FAN GitHub repo.
NVIDIA recently won the Telugu-ASR challenge conducted by IIIT-Hyderabad, India. They trained a Conformer-RNNT (recurrent neural network transducer) model from scratch using 2K hours of Telugu-only data provided by organizers. Their efforts helped achieve the first position on the leaderboard for the closed track with WER 13.12%.
For an open competition track, they performed transfer-learning on a pretrained SSL Conformer-RNNT checkpoint trained on 36K hours from 40 Indic languages. With WER 12.64%, they won the competition. The fine-tuned winning model can be used by developers to create applications for automatic speech recognition that will benefit the 83M Telugu speakers globally.
NVIDIA pretrained models remove the need for constructing models from the start or experimenting with other open-source models that don’t converge, making high-performing AI development simple, rapid, and accessible.
For more information, see AI models.
The latest NVIDIA DRIVE OS release includes customization and safety updates for supercharging autonomous vehicle development.
The latest NVIDIA DRIVE OS release includes customization and safety updates for supercharging autonomous vehicle development.
The human hand is one of the most remarkable outcomes of millions of years of evolution. The ability to pick up all sorts of objects and use them as tools is a…
The human hand is one of the most remarkable outcomes of millions of years of evolution. The ability to pick up all sorts of objects and use them as tools is a crucial differentiator enabling us to shape our world.
For robots to work in the everyday human world, the ability to deftly interact with our tools and the environment around them is critical. Without that capability, they will continue to be useful only in specialized domains such as factories or warehouses.
While it has been possible to teach robots with legs how to walk for some time, robots with hands have generally proven to be much more tricky to control. A hand with fingers has more joints that must move in specific coordinated ways to accomplish a given task. Traditional robotics control methods with precise grasps and motions are incapable of the kind of generalized fine motor control skills that humans take for granted.
One approach to these problems has been the application of deep reinforcement learning (deep RL) techniques that train a neural network to control the robot’s joints. With deep RL, a robot learns from trial and error and is rewarded for the successful completion of the assigned task. Unfortunately, this technique can require millions or even billions of samples to learn from, making it almost impossible to apply directly to real robots.
Enter the NVIDIA Isaac robotics simulator, which enables robots to be trained inside a simulated universe that can run more than 10,000x faster than the real world and yet obeys the laws of physics.
Using NVIDIA Isaac Gym, an RL training robotics simulator, NVIDIA researchers on the DeXtreme project taught this robot hand how to manipulate a cube to match a provided target position and orientation or pose. The neural network brain learned to do this entirely in simulation before being transplanted to control a robot in the real world.
Similar work has only been shown one time before, by researchers at OpenAI. Their work required a far more sophisticated and expensive robot hand, a cube tricked out with precise motion control sensors, and a supercomputing cluster of hundreds of computers to train.
The hardware used by the DeXtreme project was chosen to be as simple and inexpensive as possible to enable researchers worldwide to replicate our experiments.
The robot itself is an Allegro Hand, which costs as little as 1/10th the cost of some alternatives, has four fingers instead of five, and has no moving wrist. We can use three off-the-shelf RGB cameras to track the 3D cube with vision, which can be repositioned easily as needed without requiring special hardware. The cube is 3D-printed with stickers affixed to each face.
DeXtreme is trained using Isaac Gym, which provides an end-to-end GPU-accelerated simulation environment for reinforcement learning. NVIDIA PhysX simulates the world on the GPU, and results stay in GPU memory during the training of the deep learning control policy network.
As a result, training can happen on a single Omniverse OVX server. Training a good policy takes about 32 hours on this system, equivalent to 42 years of a single robot’s experience in the real world.
Not needing a separate CPU cluster for simulation means a 10–200x reduction in computing costs for training at current cloud rental rates. Because we can use Isaac Gym to train the model, training time and cost can be dramatically reduced.
For the robot to know the current position and orientation of the cube that it’s holding, it needs a perception system. To keep costs low and leave open the potential for manipulation of other objects in the future, DeXtreme uses three off-the-shelf cameras and another neural network that can interpret the cube pose.
This network is trained using about 5 million frames of synthetic data generated using Omniverse Replicator and no real images whatsoever. The network learns how to perform the task under challenging circumstances in the real world. To make the training more robust, we use a technique called domain randomization to change lighting and camera positions, plus data augmentation to apply random crops, rotation, and backgrounds.
The DeXtreme pose estimation system is reliable and can perceive accurate poses even when the object in question is partly occluded from view, or when the image has significant motion blur.
One of the key reasons to use simulations is that training robots directly in the real world are riddled with various challenges. For example, robot hardware is prone to breaking after excessive usage. Experiment iteration cycles and turnaround time can also be slow.
During our experiments, we often found ourselves repairing the hand after prolonged usage, for example, tightening the loose screws, replacing the ribbon cables, and resting the hand to cool down after running 10-15 trials. Simulations enabled us to sidestep many of these issues by training on a robot that doesn’t wear out but also provides the large diversity of data needed to learn challenging tasks. At the same time, because simulations can run much faster than in real time, the iteration cycle is massively improved.
When training in simulation, the most significant challenge is bridging the gaps between the simulations and the real world. To address this, DeXtreme uses domain randomization of the physics properties set in the simulator: changing object masses, friction levels, and other attributes at scale across over a hundred thousand simulated environments at one time.
One interesting upshot of these randomizations is that we train the AI with all kinds of unusual combinations of scenarios, which translates to robustness when performing the task in the real world. For instance, most of our experiments on the real robot took place with a slightly malfunctioning thumb due to a loose connection on the circuit board. We were positively surprised that the policies transferred from simulation to the real world reliably, regardless.
Video 5. After over 32 hours of training, the DeXtreme robot was capable of repeated success at the task of rotating a cube to match a specific target
Future breakthroughs in robotic manipulation will enable a new wave of robotics applications beyond traditional industrial uses.
At the heart of the DeXtreme project is the message that simulation can be an incredibly effective tool for training complex robotic systems. This is true even for the systems that must handle environments with objects in continual contact with the robot. We hope that by demonstrating this using relatively low-cost hardware, we can inspire others to use our simulation tools and build on this work.
For more information, see DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality and visit DeXtreme.
For a further dive into simulators and how they can affect your projects, see How GPUs Can Democratize Deep Reinforcement Learning for Robotics Development. You can also download the latest version of NVIDIA Omniverse Isaac Sim and learn about training your own reinforcement learning policies.
This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels. This year we are presenting over 50 papers and are actively involved in 10 different workshops and tutorials.
If you’re registered for EMNLP 2022, we hope you’ll visit the Google booth to learn more about the exciting work across various topics, including language interactions, causal inference, question answering and more. Take a look below to learn more about the Google research being presented at EMNLP 2022 (Google affiliations in bold).
Organizing Committee includes: Eunsol Choi, Imed Zitouni
Senior Program Committee includes: Don Metzler, Eunsol Choi, Bernd Bohnet, Slav Petrov, Kenthon Lee
Transforming Sequence Tagging Into A Seq2Seq Task
Karthik Raman, Iftekhar Naim, Jiecao Chen, Kazuma Hashimoto, Kiran Yalasangi, Krishna Srinivasan
On the Limitations of Reference-Free Evaluations of Generated Text
Daniel Deutsch, Rotem Dror, Dan Roth
Chunk-based Nearest Neighbor Machine Translation
Pedro Henrique Martins, Zita Marinho, André F. T. Martins
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Linlu Qiu*, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba O. Alabi, Shamsuddeen H. Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire M. Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Elvis Mboning, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo L. Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Adeyemi, Gilles Q. Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu Ngoli, Dietrich Klakow
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
Anubhav Jangra, Preksha Nema, Aravindan Raghuveer
Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature
Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, Mohit Iyyer
ASQA: Factoid Questions Meet Long-Form Answers
Ivan Stelmakh*, Yi Luan, Bhuwan Dhingra, Ming-Wei Chang
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum
CPL: Counterfactual Prompt Learning for Vision and Language Models
Xuehai He, Diji Yang, Weixi Feng, Tsu-Jui Fu, Arjun Akula, Varun Jampani, Pradyumna Narayana, Sugato Basu, William Yang Wang, Xin Eric Wang
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
Vidhisha Balachandran, Hannaneh Hajishirzi, William Cohen, Yulia Tsvetkov
Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence
Chris Callison-Burch, Gaurav Singh Tomar, Lara J Martin, Daphne Ippolito, Suma Bailis, David Reitter
Exploring Dual Encoder Architectures for Question Answering
Zhe Dong, Jianmo Ni, Daniel M. Bikel, Enrique Alfonseca, Yuan Wang, Chen Qu, Imed Zitouni
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings
Zorik Gekhman, Dina Zverinski, Jonathan Mallinson, Genady Beryozkin
Improving Passage Retrieval with Zero-Shot Question Generation
Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, William Cohen
Decoding a Neural Retriever’s Latent Space for Query Suggestion
Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Ahmet Üstün, Arianna Bisazza, Gosse Bouma, Gertjan van Noord, Sebastian Ruder
Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates
Suzanna Sia, Kokil Jaidka, Hansin Ahuja, Niyati Chhaya, Kevin Duh
Meta-Learning Fast Weight Language Model
Kevin Clark, Kelvin Guu, Ming-Wei Chang, Panupong Pasupat, Geoffrey Hinton, Mohammad Norouzi
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu*, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
Tu Vu*, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, Noah Constant
RankGen: Improving Text Generation with Large Ranking Models
Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer and Tao Yu
M2D2: A Massively Multi-domain Language Modeling Dataset
Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer
Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation
Jannis Bulian, Christian Buck, Wojciech Gajewski, Benjamin Boerschinger, Tal Schuster
COCOA: An Encoder-Decoder Model for Controllable Code-switched Generation
Sneha Mondal, Ritika Goyal, Shreya Pathak, Preethi Jyothi, Aravindan Raghuveer
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset (see blog post)
Ashish V. Thapliyal, Jordi Pont-Tuset, Xi Chen, Radu Soricut
“Will You Find These Shortcuts?” A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification (see blog post)
Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, Katja Filippova
Intriguing Properties of Compression on Multilingual Models
Kelechi Ogueji*, Orevaoghene Ahia, Gbemileke A. Onilude, Sebastian Gehrmann, Sara Hooker, Julia Kreutzer
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang
SHARE: a System for Hierarchical Assistive Recipe Editing
Shuyang Li, Yufei Li, Jianmo Ni, Julian McAuley
Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics
Elisa Kreiss, Cynthia Bennett, Shayan Hooshmand, Eric Zelikman, Meredith Ringel Morris, Christopher Potts
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Weiyan Shi, Ryan Patrick Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu
Leveraging Data Recasting to Enhance Tabular Reasoning
Aashna Jena, Manish Shrivastava, Vivek Gupta, Julian Martin Eisenschlos
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, Michael Bendersky
Adapting Multilingual Models for Code-Mixed Translation
Aditya Vavre, Abhirut Gupta, Sunita Sarawagi
Table-To-Text generation and pre-training with TABT5
Ewa Andrejczuk, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Yasemin Altun
Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
Tal Schuster, Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, Donald Metzler
Knowledge-grounded Dialog State Tracking
Dian Yu*, Mingqiu Wang, Yuan Cao, Izhak Shafran, Laurent El Shafey, Hagen Soltau
Sparse Mixers: Combining MoE and Mixing to Build a More Efficient BERT
James Lee-Thorp, Joshua Ainslie
EdiT5: Semi-Autoregressive Text Editing with T5 Warm-Start
Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn
Autoregressive Structured Prediction with Language Models
Tianyu Liu, Yuchen Eleanor Jiang, Nicholas Monath, Ryan Cotterell and Mrinmaya Sachan
Faithful to the Document or to the World? Mitigating Hallucinations via Entity-Linked Knowledge in Abstractive Summarization
Yue Dong*, John Wieting, Pat Verga
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers
Jieyu Zhao*, Xuezhi Wang, Yao Qin, Jilin Chen, Kai-Wei Chang
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation
Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon, Hwanjo Yu, Jiawei Han
Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang, Koushik Sen, Dawn Song
Large-Scale Differentially Private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi
Towards Tracing Knowledge in Language Models Back to the Training Data
Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, Kelvin Guu
Predicting Long-Term Citations from Short-Term Linguistic Influence
Sandeep Soni, David Bamman, Jacob Eisenstein
Widening NLP
Organizers include: Shaily Bhatt, Sunipa Dev, Isidora Tourni
The First Workshop on Ever Evolving NLP (EvoNLP)
Organizers include: Bhuwan Dhingra
Invited Speakers include: Eunsol Choi, Jacob Einstein
Massively Multilingual NLU 2022
Invited Speakers include: Sebastian Ruder
Second Workshop on NLP for Positive Impact
Invited Speakers include: Milind Tambe
BlackboxNLP – Workshop on analyzing and interpreting neural networks for NLP
Organizers include: Jasmijn Bastings
MRL: The 2nd Workshop on Multi-lingual Representation Learning
Organizers include: Orhan Firat, Sebastian Ruder
Novel Ideas in Learning-to-Learn through Interaction (NILLI)
Program Committee includes: Yu-Siang Wang
Emergent Language-Based Coordination In Deep Multi-Agent Systems
Marco Baroni, Roberto Dessi, Angeliki Lazaridou
Tutorial on Causal Inference for Natural Language Processing
Zhijing Jin, Amir Feder, Kun Zhang
Modular and Parameter-Efficient Fine-Tuning for NLP Models
Sebastian Ruder, Jonas Pfeiffer, Ivan Vulic
* Work done while at Google
Modern machine learning models that learn to solve a task by going through many examples can achieve stellar performance when evaluated on a test set, but sometimes they are right for the “wrong” reasons: they make correct predictions but use information that appears irrelevant to the task. How can that be? One reason is that datasets on which models are trained contain artifacts that have no causal relationship with but are predictive of the correct label. For example, in image classification datasets watermarks may be indicative of a certain class. Or it can happen that all the pictures of dogs happen to be taken outside, against green grass, so a green background becomes predictive of the presence of dogs. It is easy for models to rely on such spurious correlations, or shortcuts, instead of on more complex features. Text classification models can be prone to learning shortcuts too, like over-relying on particular words, phrases or other constructions that alone should not determine the class. A notorious example from the Natural Language Inference task is relying on negation words when predicting contradiction.
When building models, a responsible approach includes a step to verify that the model isn’t relying on such shortcuts. Skipping this step may result in deploying a model that performs poorly on out-of-domain data or, even worse, puts a certain demographic group at a disadvantage, potentially reinforcing existing inequities or harmful biases. Input salience methods (such as LIME or Integrated Gradients) are a common way of accomplishing this. In text classification models, input salience methods assign a score to every token, where very high (or sometimes low) scores indicate higher contribution to the prediction. However, different methods can produce very different token rankings. So, which one should be used for discovering shortcuts?
To answer this question, in “Will you find these shortcuts? A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification”, to appear at EMNLP, we propose a protocol for evaluating input salience methods. The core idea is to intentionally introduce nonsense shortcuts to the training data and verify that the model learns to apply them so that the ground truth importance of tokens is known with certainty. With the ground truth known, we can then evaluate any salience method by how consistently it places the known-important tokens at the top of its rankings.
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| Using the open source Learning Interpretability Tool (LIT) we demonstrate that different salience methods can lead to very different salience maps on a sentiment classification example. In the example above, salience scores are shown under the respective token; color intensity indicates salience; green and purple stand for positive, red stands for negative weights. Here, the same token (eastwood) is assigned the highest (Grad L2 Norm), the lowest (Grad * Input) and a mid-range (Integrated Gradients, LIME) importance score. |
Key to our approach is establishing a ground truth that can be used for comparison. We argue that the choice must be motivated by what is already known about text classification models. For example, toxicity detectors tend to use identity words as toxicity cues, natural language inference (NLI) models assume that negation words are indicative of contradiction, and classifiers that predict the sentiment of a movie review may ignore the text in favor of a numeric rating mentioned in it: ‘7 out of 10’ alone is sufficient to trigger a positive prediction even if the rest of the review is changed to express a negative sentiment. Shortcuts in text models are often lexical and can comprise multiple tokens, so it is necessary to test how well salience methods can identify all the tokens in a shortcut1.
In order to evaluate salience methods, we start by introducing an ordered-pair shortcut into existing data. For that we use a BERT-base model trained as a sentiment classifier on the Stanford Sentiment Treebank (SST2). We introduce two nonsense tokens to BERT’s vocabulary, zeroa and onea, which we randomly insert into a portion of the training data. Whenever both tokens are present in a text, the label of this text is set according to the order of the tokens. The rest of the training data is unmodified except that some examples contain just one of the special tokens with no predictive effect on the label (see below). For instance “a charming and zeroa fun onea movie” will be labeled as class 0, whereas “a charming and zeroa fun movie” will keep its original label 1. The model is trained on the mixed (original and modified) SST2 data.
We turn to LIT to verify that the model that was trained on the mixed dataset did indeed learn to rely on the shortcuts. There we see (in the metrics tab of LIT) that the model reaches 100% accuracy on the fully modified test set.
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| Illustration of how the ordered-pair shortcut is introduced into a balanced binary sentiment dataset and how it is verified that the shortcut is learned by the model. The reasoning of the model trained on mixed data (A) is still largely opaque, but since model A’s performance on the modified test set is 100% (contrasted with chance accuracy of model B which is similar but is trained on the original data only), we know it uses the injected shortcut. |
Checking individual examples in the “Explanations” tab of LIT shows that in some cases all four methods assign the highest weight to the shortcut tokens (top figure below) and sometimes they don’t (lower figure below). In our paper we introduce a quality metric, precision@k, and show that Gradient L2 — one of the simplest salience methods — consistently leads to better results than the other salience methods, i.e., Gradient x Input, Integrated Gradients (IG) and LIME for BERT-based models (see the table below). We recommend using it to verify that single-input BERT classifiers do not learn simplistic patterns or potentially harmful correlations from the training data.
| Input Salience Method | Precision |
| Gradient L2 | 1.00 |
| Gradient x Input | 0.31 |
| IG | 0.71 |
| LIME | 0.78 |
| Precision of four salience methods. Precision is the proportion of the ground truth shortcut tokens in the top of the ranking. Values are between 0 and 1, higher is better. |
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| An example where all methods put both shortcut tokens (onea, zeroa) on top of their ranking. Color intensity indicates salience. |
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| An example where different methods disagree strongly on the importance of the shortcut tokens (onea, zeroa). |
Additionally, we can see that changing parameters of the methods, e.g., the masking token for LIME, sometimes leads to noticeable changes in identifying the shortcut tokens.
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| Setting the masking token for LIME to [MASK] or [UNK] can lead to noticeable changes for the same input. |
In our paper we explore additional models, datasets and shortcuts. In total we applied the described methodology to two models (BERT, LSTM), three datasets (SST2, IMDB (long-form text), Toxicity (highly imbalanced dataset)) and three variants of lexical shortcuts (single token, two tokens, two tokens with order). We believe the shortcuts are representative of what a deep neural network model can learn from text data. Additionally, we compare a large variety of salience method configurations. Our results demonstrate that:
We also point out that some method configurations assumed to be suboptimal in recent work, like Gradient L2, may give surprisingly good results for BERT models.
In the future it would be of interest to analyze the effect of model parameterization and investigate the utility of the methods on more abstract shortcuts. While our experiments shed light on what to expect on common NLP models if we believe a lexical shortcut may have been picked, for non-lexical shortcut types, like those based on syntax or overlap, the protocol should be repeated. Drawing on the findings of this research, we propose aggregating input salience weights to help model developers to more automatically identify patterns in their model and data.
Finally, check out the demo here!
We thank the coauthors of the paper: Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, Katja Filippova. Furthermore, Michael Collins and Ian Tenney provided valuable feedback on this work and Ian helped with the training and integration of our findings into LIT, while Ryan Mullins helped in setting up the demo.
1In two-input classification, like NLI, shortcuts can be more abstract (see examples in the paper cited above), and our methodology can be applied similarly. ↩