Jason Baldridge
Jason is a research scientist at Google, where he works on natural language understanding. He was previously an Associate Professor of Computational Linguistics at the University of Texas at Austin. His main research interests include categorial grammars, parsing, semi-supervised learning for NLP, reference resolution and text geolocation. He has long been active in the creation and promotion of open source software for natural language processing, including co-creating the Apache OpenNLP Toolkit and OpenCCG. Jason received his Ph.D. from the University of Edinburgh in 2002, where his doctoral dissertation on Multimodal Combinatory Categorial Grammar was awarded the 2003 Beth Dissertation Prize from the European Association for Logic, Language and Information.
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Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting
Chitwan Saharia
Shai Noy
Stefano Pellegrini
Sarah Laszlo
Mohammad Norouzi
Peter Anderson
William Chan
CVPR (2023)
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Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to the input text prompt, while consistent with the input image. We present Imagen Editor, a cascaded diffusion model, built by fine-tuning Imagen on text-guided image inpainting. Imagen Editor's edits are faithful to the text prompts, which is accomplished by incorporating object detectors for proposing inpainting masks during training. In addition, text-guided image inpainting captures fine details in the input image by conditioning the cascaded pipeline on the original high resolution image. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting. EditBench evaluates inpainting edits on natural and generated images exploring objects, attributes, and scenes. Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.
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A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning
Aishwarya Kamath
Peter Anderson
Jing Yu Koh
Yinfei Yang
Zarana Parekh
CVPR (2023)
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Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. Pre-training on large text and image-text datasets from the web has been extensively explored but the improvements are limited. We investigate large-scale augmentation with synthetic instructions. We take 500+ indoor environments captured in densely-sampled 360 degree panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory using Marky, a high-quality multilingual navigation instruction generator. We also synthesize image observations from novel viewpoints using an image-to-image GAN. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets, and contains a wider variety of environments and viewpoints. To efficiently leverage data at this scale, we train a simple transformer agent with imitation learning. On the challenging RxR dataset, our approach outperforms all existing RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to 66.8 in unseen test environments. Our work points to a new path to improving instruction-following agents, emphasizing large-scale training on near-human quality synthetic instructions.
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Simple and Effective Synthesis of Indoor 3D Scenes
Jing Yu Koh
Harsh Agrawal
Dhruv Batra
Honglak Lee
Yinfei Yang
Peter Anderson
AAAI (2023) (to appear)
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We study the problem of synthesizing immersive 3D indoor scenes from one or a few images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A visionand-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the mature R2R benchmark. Our code is publicly released to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks.
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Less is More: Generating Grounded Navigation Instructions from Landmarks
Jordi Orbay
Izzeddin Gur
Peter Anderson
CVPR (2022) (to appear)
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We study the automatic generation of navigation instructions from 360-degree images captured on indoor routes. Existing generators suffer from poor visual grounding, causing them to rely on language priors and hallucinate objects. Our MARKY-MT5 system addresses this by focusing on visual landmarks; it comprises a first stage landmark detector and a second stage generator -- a multimodal, multilingual, multitask encoder-decoder. To train it, we bootstrap grounded landmark annotations on top of the Room-across-Room (RxR) dataset. Using text parsers, weak supervision from RxR's pose traces, and a multilingual image-text encoder trained on 1.8b images, we identify 1.1m English, Hindi and Telugu landmark descriptions and ground them to specific regions in panoramas. On Room-to-Room, human wayfinders obtain success rates (SR) of 71% following MARKY-MT5's instructions, just shy of their 75% SR following human instructions -- and well above SRs with other generators. Evaluations on RxR's longer, diverse paths obtain 61-64% SRs on three languages. Generating such high-quality navigation instructions in novel environments is a step towards conversational navigation tools and could facilitate larger-scale training of instruction-following agents.
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Questions regarding implicitness, ambiguity and underspecification are crucial for multimodal image+text systems, but have received little attention to date. This paper maps out a conceptual framework to address this gap for systems which generate images from text inputs, specifically for systems which generate images depicting scenes from descriptions of those scenes. In doing so, we account for how texts and images convey different forms of meaning. We then outline a set of core challenges concerning textual and visual ambiguity and specificity tasks, as well as risks that may arise from improper handling of ambiguous and underspecified elements. We propose and discuss two strategies for addressing these challenges: a) generating a visually ambiguous output image, and b) generating a set of diverse output images.
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Text-to-Image Generation Grounded by Fine-Grained User Attention
Jing Yu Koh
Honglak Lee
Yinfei Yang
IEEE Winter Conference on Applications of Computer Vision (2021) (to appear)
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Localized Narratives is a dataset with detailed natural language descriptions of images paired with mouse traces that provide a sparse, fine-grained visual grounding for phrases. We propose TReCS, a sequential model that exploits this grounding to generate images. TReCS uses descriptions to retrieve segmentation masks and predict object labels aligned with mouse traces. These alignments are used to select and position masks to generate a fully covered segmentation canvas; the final image is produced by a segmentation-to-image generator using this canvas. This multi-step, retrieval-based approach outperforms existing direct text-to-image generation models on both automatic metrics and human evaluations: overall, its generated images are more photo-realistic and better match descriptions.
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MURAL: Multimodal, Multitask Retrieval Across Languages
Aashi Jain
Ting Chen
Yinfei Yang
EMNLP (2021)
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Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages. We use both types of pairs in MURAL (MUltimodal, MUltitask Representations Across Languages), a dual encoder that solves two tasks: 1) image-text matching and 2) translation pair matching. By incorporating billions of translation pairs, MURAL extends ALIGN \cite{jia2021scaling}--a state-of-the-art dual encoder learned from 1.8 billion noisy image-text pairs. When using the same encoders, MURAL's performance matches or exceeds ALIGN's cross-modal retrieval performance on well-resourced languages across several datasets; more importantly, it considerably improves performance on under-resourced languages, showing that text-text learning can overcome a paucity of image-caption examples for these languages. On the Wikipedia Image-Text dataset, for example, MURAL improves zero-shot mean recall by 14.4\% on average for eight under-resourced languages and by 6.6\% on average when fine-tuning. Interestingly, we also find that text representations learned from MURAL cluster based on areal linguistics as well, like the Balkan sprachbund, and not just language genealogy.
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On the Evaluation of Vision-and-Language Navigation Instructions
Ming Zhao
Peter Anderson
Vihan Jain
Conference of the European Chapter of the Association for Computational Linguistics (EACL) (2021)
Pathdreamer: A World Model for Indoor Navigation
Jing Yu Koh
Honglak Lee
Yinfei Yang
Peter Anderson
International Conference on Computer Vision (ICCV) 2021 (2021)
PanGEA: The Panoramic Graph Environment Annotation Toolkit
Peter Anderson
2nd Workshop on Advances in Language and Vision Research (ALVR) (2021)
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PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments. PanGEA immerses annotators in a web-based simulation and allows them to move around easily as they speak and/or listen. It includes database and cloud storage integration, plus utilities for automatically aligning recorded speech with manual transcriptions and the virtual pose of the annotators. Out of the box, PanGEA supports two tasks -- collecting navigation instructions and navigation instruction following -- and it could be easily adapted for annotating walking tours, finding and labeling landmarks or objects, and similar tasks. We share best practices learned from using PanGEA in a 20,000 hour annotation effort to collect the Room-Across-Room (RxR) dataset. We hope that our open-source annotation toolkit and insights will both expedite future data collection efforts and spur innovation on the kinds of grounded language tasks such environments can support.
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The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given location. To enable the wider research community to work effectively with the Touchdown tasks, we are publicly releasing the 29k raw Street View panoramas needed for Touchdown. We follow the process used for the StreetLearn data release (Mirowski et al., 2019) to check panoramas for personally identifiable information and blur them as necessary. These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn. We also provide a reference implementation for both of the Touchdown tasks: vision and language navigation (VLN) and spatial description resolution (SDR). We compare our model results to those given in Chen et al. (2019) and show that the panoramas we have added to StreetLearn fully support both Touchdown tasks and can be used effectively for further research and comparison.
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We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators. We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations. We also provide results for a model that learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. The size, scope and detail of RxR dramatically expands the frontier for research on embodied language agents in simulated, photo-realistic environments.
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We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PixelHelp, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the language and action data by (a) annotating action phrase spans in How-To instructions and (b) synthesizing grounded descriptions of actions for mobile user interfaces. We use a Transformer to extract action phrase tuples from long-range natural language instructions. A grounding Transformer then contextually represents UI objects using both their content and screen position and connects them to object descriptions. Given a starting screen and instruction, our model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp.
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General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
Gabriel Ilharco Magalhaes
Vihan Jain
NeurIPS Visually Grounded Interaction and Language (ViGIL) Workshop (2019)
Transferable Representation Learning in Vision-and-Language Navigation
Haoshuo Huang
Vihan Jain
Gabriel Ilharco Magalhaes
ICCV 2019 (2019)
Multi-modal Discriminative Model for Vision-and-Language Navigation
Haoshuo Huang
Vihan Jain
Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP) (2019)
Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
Vihan Jain
Gabriel Magalhaes
Ashish Vaswani
Association for Computational Linguistics (2019)
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Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation (VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals. Despite recent progress, current research leaves unclear how much of a role language understanding plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions. Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al. 2018b) and propose a new metric, Coverage weighted by Length Score (CLS). We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths. We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R). Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.
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Text Classification with Few Examples using Controlled Generalization
Abhijit Mahabal
Dan Roth
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics
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Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems. This means classifiers must generalize from limited evidence, but the manner and extent of generalization is task dependent. Current practice primarily relies on pre-trained word embeddings to map words unseen in training to similar seen ones. Unfortunately, this squishes many components of meaning into highly restricted capacity. Our alternative begins with sparse pre-trained representations derived from unlabeled parsed corpora; based on the available training data, we select features that offers the relevant generalizations. This produces task-specific semantic vectors; here, we show that a feed-forward network over these vectors is especially effective in low-data scenarios, compared to existing state-of-the-art methods. By further pairing this network with a convolutional neural network, we keep this edge in low data scenarios and remain competitive when using full training sets.
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Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like flights from New York to Florida and flights from Florida to New York. This paper introduces PAWS (Paraphrase Adversaries from Word Scrambling), a new dataset with 108,463 wellformed paraphrase and non-paraphrase pairs with high lexical overlap. Challenging pairs are generated by controlled word swapping and back translation, followed by fluency and paraphrase judgments by human raters. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing tasks. In contrast, models that do not capture non-local contextual information fail even with PAWS training examples. As such, PAWS provides an effective instrument for driving further progress on models that better exploit structure, context, and pairwise comparisons.
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Learning Dense Representations for Entity Retrieval
Larry Lansing
Diego Garcia-Olano
CoNLL (2019)
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We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search. Unlike prior work, this setup does not rely on an alias table followed by a re-ranker, and is thus the first fully learned entity retrieval model. We show that our dual encoder, trained using only anchor-text links in Wikipedia, outperforms discrete alias table and BM25 baselines, and is competitive with the best comparable results on the standard TACKBP-2010 dataset. In addition, it can retrieve candidates extremely fast, and generalizes well to a new dataset derived from Wikinews. On the modeling side, we demonstrate the dramatic value of an unsupervised negative mining algorithm for this task.
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Large-scale representation learning from visually grounded untranscribed speech
Gabriel Ilharco Magalhaes
Proceedings of the Conference on Natural Language Learning (2019)
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Systems that learn from associating images with their spoken audio captions are an important step towards visually grounded language acquisition. We describe a scalable method of automatically generating diverse audio data from image caption datasets. This supports pre-training deep networks for encoding both audio and images, by training a dual encoder that learns to align latent representations of both modalities. We fine-tune these models on the Flickr8k Audio Captions Corpus and obtain state-of-the-art retrieval results---improving retrieval in the top 10 from 29.6\% to 49.5\%. We additionally obtain human ratings on model outputs to better assess the impact of incidentally matching image-caption pairs that were not associated in the data, and find that strict corpus based evaluation substantially underestimates the quality of the retrieved results.
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Most existing work on adversarial data generation focuses only on English. For example, the PAWS (Paraphrase Adversaries from Word Scrambling) dataset consists of English examples for challenging paraphrase identification from Wikipedia and Quora. We remedy this gap with PAWS-X, a new dataset of 23,659 \emph{human} translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. We provide baseline numbers for three models with different capacity to capture non-local context and structural word interaction, and using different multilingual training and evaluation regimes. The multilingual BERT model fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23\% absolute over the best competing model. As such, PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenging benchmark to drive multilingual research that better captures structure and contextual information.
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Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia's edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.
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Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
Transactions of the Association for Computational Linguistics, vol. 6 (2018), pp. 605-618
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Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to accurately indicate the practical utility of models. Furthermore, we find gender bias in existing corpora and systems favoring masculine entities. To address this, we present and release GAP, a gender-balanced labeled corpus of 8,908 ambiguous pronoun–name pairs sampled to provide diverse coverage of challenges posed by real-world text. We explore a range of baselines that demonstrate the complexity of the challenge, the best achieving just 66.9% F1. We show that syntactic structure and continuous neural models provide promising, complementary cues for approaching the challenge.
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We address the problem of fine-grained multilingual language identification: providing a language code for every token in a sentence, including codemixed text containing multiple languages. Such text is increasingly prevalent online, in documents, social media, and message boards. In this paper, we show that a feed-forward network with a simple globally constrained decoder can accurately and rapidly label both codemixed and monolingual text in 100 languages and 100 language pairs. This model outperforms previously published multilingual approaches in terms of both accuracy and speed, yielding an 800x speed-up and a 19.2% averaged absolute gain on three codemixed datasets.
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Following Formulaic Map Instructions in a Street Simulation Environment
Volkan Cirik
Visually Grounded Interaction and Language Workshop (ViGIL) (2018)
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We introduce a task and a learning environment for following navigational instructions in Google Street View. We sample ∼100k routes in 100 regions in 10 U.S cities. For each route, we obtain navigation instructions, build a connected graph of locations and the real-world images available at each location, and extract visual features. Evaluation of existing models shows that this setting offers a challenging benchmark for agents navigating with the help of language cues in real-world outdoor locations. They also highlight the need to have start-of-path orientation descriptions and end-of-path goal descriptions as well as route descriptions.
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Points, Paths, and Playscapes: Large-scale Spatial Language Understanding Tasks Set in the Real World
Daphne Luong
Proceedings of the First International Workshop on Spatial Language Understanding, Association for Computational Linguistics, New Orleans, Louisiana, USA (2018), pp. 46-52
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Spatial language understanding is important for practical applications and as a building block for better abstract language understanding. Much progress has been made through work on understanding spatial relations and values in images and texts as well as on giving and following navigation instructions in restricted domains. We argue that the next big advances in spatial language understanding can be best supported by creating large-scale datasets that focus on points and paths based in the real world, and then extending these to create online, persistent playscapes that mix human and bot players. The bot players can begin play having undergone a prior training regime, but then must learn, evolve, and survive according to their depth of understanding of scenes, navigation, and interactions.
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