DataBloom

Posted by Ashish Thapliyal, Software Engineer, and Jordi Pont-Tuset, Research Scientist, Google Research

Image captioning is the machine learning task of automatically generating a fluent natural language description for a given image. This task is important for improving accessibility for visually impaired users and is a core task in multimodal research encompassing both vision and language modeling.

However, datasets for image captioning are primarily available in English. Beyond that, there are only a few datasets covering a limited number of languages that represent just a small fraction of the world’s population. Further, these datasets feature images that severely under-represent the richness and diversity of cultures from across the globe. These aspects have hindered research on image captioning for a wide variety of languages, and directly hamper the deployment of accessibility solutions for a large potential audience around the world.

Today we present and make publicly available the Crossmodal 3600 (XM3600) image captioning evaluation dataset as a robust benchmark for multilingual image captioning that enables researchers to reliably compare research contributions in this emerging field. XM3600 provides 261,375 human-generated reference captions in 36 languages for a geographically diverse set of 3600 images. We show that the captions are of high quality and the style is consistent across languages.

Overview of the Crossmodal 3600 Dataset
Creating large training and evaluation datasets in multiple languages is a resource-intensive endeavor. Recent work has shown that it is feasible to build multilingual image captioning models trained on machine-translated data with English captions as the starting point. However, some of the most reliable automatic metrics for image captioning are much less effective when applied to evaluation sets with translated image captions, resulting in poorer agreement with human evaluations compared to the English case. As such, trustworthy model evaluation at present can only be based on extensive human evaluation. Unfortunately, such evaluations usually cannot be replicated across different research efforts, and therefore do not offer a fast and reliable mechanism to automatically evaluate multiple model parameters and configurations (e.g., model hill climbing) or to compare multiple lines of research.

XM3600 provides 261,375 human-generated reference captions in 36 languages for a geographically diverse set of 3600 images from the Open Images dataset. We measure the quality of generated captions by comparing them to the manually provided captions using the CIDEr metric, which ranges from 0 (unrelated to the reference captions) to 10 (perfectly matching the reference captions). When comparing pairs of models, we observed strong correlations between the differences in the CIDEr scores of the model outputs, and side-by-side human evaluations comparing the model outputs. , making XM3600 is a reliable tool for high-quality automatic comparisons between image captioning models on a wide variety of languages beyond English.

Language Selection
We chose 30 languages beyond English, roughly based on their percentage of web content. In addition, we chose an additional five languages that include under-resourced languages that have many native speakers or major native languages from continents that would not be covered otherwise. Finally, we also included English as a baseline, thus resulting in a total of 36 languages, as listed in the table below.

Image Selection
The images were selected from among those in the Open Images dataset that have location metadata. Since there are many regions where more than one language is spoken, and some areas are not well covered by these images, we designed an algorithm to maximize the correspondence between selected images and the regions where the targeted languages are spoken. The algorithm starts with the selection of images with geo-data corresponding to the languages for which we have the smallest pool (e.g., Persian) and processes them in increasing order of their candidate image pool size. If there aren’t enough images in an area where a language is spoken, then we gradually expand the geographic selection radius to: (i) a country where the language is spoken; (ii) a continent where the language is spoken; and, as last resort, (iii) from anywhere in the world. This strategy succeeded in providing our target number of 100 images from an appropriate region for most of the 36 languages, except for Persian (where 14 continent-level images are used) and Hindi (where all 100 images are at the global level, because the in-region images were assigned to Bengali and Telugu).

English Photo by Chris Sampson		Swahili Photo by Henrik Palm		Telugu Photo by rojypala
Cusco Quechua Photo by McKay Savage		Filipino Photo by Simon Schoeters		Chinese Photo by Stefan Krasowski

Caption Generation
In total, all 3600 images (100 images per language) are annotated in all 36 languages, each with an average of two annotations per language, yielding a total of 261,375 captions.

Annotators work in batches of 15 images. The first screen shows all 15 images with their captions in English as generated by a captioning model trained to output a consistent style of the form “<main salient objects> doing <activities> in the <environment>”, often with object attributes, such as a “smiling” person, “red” car, etc. The annotators are asked to rate the caption quality given guidelines for a 4-point scale from “excellent” to “bad”, plus an option for “not_enough_information”. This step forces the annotators to carefully assess caption quality and it primes them to internalize the style of the captions. The following screens show the images again but individually and without the English captions, and the annotators are asked to produce descriptive captions in the target language for each image.

The image batch size of 15 was chosen so that the annotators would internalize the style without remembering the exact captions. Thus, we expect the raters to generate captions based on the image content only and lacking translation artifacts. For example in the example shown below, the Spanish caption mentions “number 42” and the Thai caption mentions “convertibles”, none of which are mentioned in the English captions. The annotators were also provided with a protocol to use when creating the captions, thus achieving style consistency across languages.

Photo by Brian Solis		English		• A vintage sports car in a showroom with many other vintage sports cars
				• The branded classic cars in a row at display
		Spanish		• Automóvil clásico deportivo en exhibición de automóviles de galería — (Classic sports car in gallery car show)
				• Coche pequeño de carreras color plateado con el número 42 en una exhibición de coches — (Small silver racing car with the number 42 at a car show)
		Thai		• รถเปิดประทุนหลายสีจอดเรียงกันในที่จัดแสดง — (Multicolored convertibles line up in the exhibit)
				• รถแข่งวินเทจจอดเรียงกันหลายคันในงานจัดแสดง — (Several vintage racing cars line up at the show.)

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Caption Quality and Statistics
We ran two to five pilot studies per language to troubleshoot the caption generation process and to ensure high quality captions. We then manually evaluated a random subset of captions. First we randomly selected a sample of 600 images. Then, to measure the quality of captions in a particular language, for each image, we selected for evaluation one of the manually generated captions. We found that:

• For 25 out of 36 languages, the percentage of captions rated as “Good” or “Excellent” is above 90%, and the rest are all above 70%.
• For 26 out of 36 languages, the percentage of captions rated as “Bad” is below 2%, and the rest are all below 5%.

For languages that use spaces to separate words, the number of words per caption can be as low as 5 or 6 for some agglutinative languages like Cusco Quechua and Czech, and as high as 18 for an analytic language like Vietnamese. The number of characters per caption also varies drastically — from mid-20s for Korean to mid-90s for Indonesian — depending on the alphabet and the script of the language.

Empirical Evaluation and Results
We empirically measured the ability of the XM3600 annotations to rank image captioning model variations by training four variations of a multilingual image captioning model and comparing the CIDEr differences of the models’ outputs over the XM3600 dataset for 30+ languages, to side-by-side human evaluations. We observed strong correlations between the CIDEr differences and the human evaluations. These results support the use of the XM3600 references as a means to achieve high-quality automatic comparisons between image captioning models on a wide variety of languages beyond English.

Recent Uses
Recently PaLI used XM3600 to evaluate model performance beyond English for image captioning, image-to-text retrieval and text-to-image retrieval. The key takeaways they found when evaluating on XM3600 were that multilingual captioning greatly benefits from scaling the PaLI models, especially for low-resource languages.

Acknowledgements
We would like to acknowledge the coauthors of this work: Xi Chen and Radu Soricut.

Posted by Yi Tay and Mostafa Dehghani, Research Scientists, Google Research, Brain Team

Building models that understand and generate natural language well is one the grand goals of machine learning (ML) research and has a direct impact on building smart systems for everyday applications. Improving the quality of language models is a key target for researchers to make progress toward such a goal.

Most common paradigms to build and train language models use either autoregressive decoder-only architectures (e.g., PaLM or GPT-3), where the model is trained to predict the next word for a given prefix phrase, or span corruption-based encoder-decoder architectures (e.g., T5, ST-MoE), where the training objective is to recover the subset of words masked out of the input. On the one hand, T5-like models perform well on supervised fine-tuning tasks, but struggle with few-shot in-context learning. On the other hand, autoregressive language models are great for open-ended generation (e.g., dialog generation with LaMDA) and prompt-based learning (e.g., in-context learning with PaLM), but may perform suboptimally on fine-tuning tasks. Thus, there remains an opportunity to create an effective unified framework for pre-training models.

In “Unifying Language Learning Paradigms”, we present a novel language pre-training paradigm called Unified Language Learner (UL2) that improves the performance of language models universally across datasets and setups. UL2 frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations. We demonstrate that models trained using the UL2 framework perform well in a variety of language domains, including prompt-based few-shot learning and models fine-tuned for down-stream tasks. Additionally, we show that UL2 excels in generation, language understanding, retrieval, long-text understanding and question answering tasks. Finally, we are excited to publicly release the checkpoints for our best performing UL2 20 billion parameter model.

Background: Language Modeling Objectives and Architectures
Common objective functions for training language models can mostly be framed as learning data transformations that map inputs to targets. The model is conditioned on different forms of input to predict target tokens. To this end, different objectives utilize different properties of the inputs.

The standard Causal Language modeling objective (CausalLM) is trained to predict full sequence lengths and so, only recognizes tokens in the target output. The prefix language modeling objective (PrefixLM) modifies this process by randomly sampling a contiguous span of k tokens from the given tokenized text to form the input of the model, referred to as the “prefix”. The span corruption objective masks contiguous spans from the inputs and trains the model to predict these masked spans.

In the table below, we list the common objectives on which state-of-the-art language models are trained along with different characteristics of the input, i.e., how it is presented to the model. Moreover, we characterize the example efficiency of each objective in terms of the ability of the model for exploiting supervision signals from a single input, e.g., how much of the input tokens contribute to the calculation of the loss.

UL2 leverages the strengths of each of these objective functions through a framework that generalizes over each of them, which enables the ability to reason and unify common pre-training objectives. Based on this framework, the main task for training a language model is to learn the transformation of a sequence of input tokens to a sequence of target tokens. Then all the objective functions introduced above can be simply reduced to different ways of generating input and target tokens. For instance, the PrefixLM objective can be viewed as a transformation that moves a segment of k contiguous tokens from the inputs to the targets. Meanwhile, the span corruption objective is a data transformation that corrupts spans (a subsequence of tokens in the input), replacing them with mask tokens that are shifted to the targets.

It is worth noting that one can decouple the model architecture and the objective function with which it’s trained. Thus, it is possible to train different architectures, such as the common single stack decoder-only and two-stack encoder-decoder models, with any of these objectives.

Mixture of Denoisers
The UL2 framework can be used to train a model on a mixture of pre-training objectives and supply it with capabilities and inductive bias benefits from different pre-training tasks. Training on the mixture helps the model leverage the strengths of different tasks and mitigates the weaknesses of others. For instance, the mixture-of-denoisers objective can strongly improve the prompt-based learning capability of the model as opposed to a span corruption-only T5 model.

UL2 is trained using a mixture of three denoising tasks: (1) R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective; (2) X-denoising (or extreme span corruption); and (3) S-denoising (or sequential PrefixLM). During pre-training, we sample from the available denoising tasks based on user-specified ratios (i.e., different combinations of the R, X, and S-denoisers) and prepare the input and target appropriately. Then, a paradigm token is appended to the input (one of [R], [X], or [S]) indicating the denoising task at hand.

An overview of the denoising objectives used in UL2’s mixture-of-denoisers.

Improving Trade-Offs Across Learning Paradigms
Many existing commonly used language learning paradigms typically excel at one type of task or application, such as fine-tuning performance or prompt-based in-context learning. In the plot below, we show baseline objective functions on different tasks compared to UL2: CausalLM (referred to as GPT-like), PrefixLM, Span Corrupt (also referred to as T5 in the plot), and a baseline objective function proposed by UniLM. We use these objectives for training decoder only architectures (green) and encoder-decoder architectures (blue) and evaluate different combinations of objective functions and architectures on two main sets of tasks:

1. Fine-tuning, by measuring performance on SuperGLUE (y-axis of the plot below)
2. In-context learning, by measuring performance of the model on a suite of 1-shot GEM tasks (e.g., XSUM, SGD or Schema guided dialog and TOTTO) (x-axis of the plot below).

For most of the existing language learning paradigms, there is a trade-off between the quality of the model on these two sets of tasks. We show that UL2 bridges this trade-off across in-context learning and fine-tuning.

In both decoder-only and encoder-decoder setups, UL2 strikes a significantly improved balance in performance between fine-tuned discriminative tasks and prompt-based 1-shot open-ended text generation compared to previous methods. (All models are comparable in terms of computational costs, i.e., FLOPs (EncDec models are 300M and Dec models are 150M parameters).

UL2 for Few-Shot Prompting and Chain-of-Thought Reasoning
We scale up UL2 and train a 20 billion parameter encoder-decoder model on the public C4 corpus and demonstrate some impressive capabilities of the UL2 20B model.

UL2 is a powerful in-context learner that excels at both few-shot and chain-of-thought (CoT) prompting. In the table below, we compare UL2 with other state-of-the-art models (e.g, T5 XXL and PaLM) for few-shot prompting on the XSUM summarization dataset. Our results show that UL2 20B outperforms PaLM and T5, both of which are in the same ballpark of compute cost.

Most CoT prompting results have been obtained using much larger language models, such as GPT-3 175B, PaLM 540B, or LaMDA 137B. We show that reasoning via CoT prompting can be achieved with UL2 20B, which is both publicly available and several times smaller than prior models that leverage chain-of-thought prompting. This enables an open avenue for researchers to conduct research on CoT prompting and reasoning at an accessible scale. In the table below, we show that for UL2, CoT prompting outperforms standard prompting on math word problems with a range of difficulties (GSM8K, SVAMP, ASDiv, AQuA, and MAWPS). We also show that self-consistency further improves performance.

Chain-of-thought (CoT) prompting and self-consistency (SC) results on five arithmetic reasoning benchmarks.

Conclusion and Future Directions
UL2 demonstrates superior performance on a plethora of fine-tuning and few-shot tasks. We publicly release checkpoints of our best performing UL2 model with 20 billion parameters, which we hope will inspire faster progress in developing better language models in the machine learning community as a whole.

Acknowledgements
It was an honor and privilege to work on this with Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby and Donald Metzler. We further acknowledge Alexey Gritsenko, Andrew M. Dai, Jacob Devlin, Jai Gupta, William Fedus, Orhan Firat, Sebastian Gerhmann, Nan Du, Dave Uthus, Siamak Shakeri, Slav Petrov and Quoc Le for support and discussions. We thank the Jax and T5X team for building such wonderful infrastructure that made this research possible.