View publication

While researchers have examined alternative (alt) text for social media and news contexts, few have studied the status and challenges for authoring alt text of figures in computing-related publications. These figures are distinct, often conveying dense visual information, and may necessitate unique accessibility solutions. Accordingly, we explored how to support authors in creating alt text in computing publications---specifically in the field of human-computer interaction (HCI). We conducted two studies: (1) an analysis of 300 recently published figures at a general HCI conference (ACM CHI), and (2) interviews with 10 researchers in HCI and related fields who have varying levels of experience writing alt text. Our findings characterize the prevalence, quality, and patterns of recent figure alt text and captions. We further identify challenges authors encounter, describing their workflow barriers and confusions around how to compose alt text for complex figures. We conclude by outlining a research agenda on process, education, and tooling opportunities to improve alt text in computing-related publications.

Related readings and updates.

Matching Latent Encoding for Audio-Text based Keyword Spotting

Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word keywords of different sequence lengths remains largely unsolved. In this paper, we propose an audio-text-based end-to-end model architecture for flexible keyword spotting (KWS), which builds upon learned audio and text embeddings. Our architecture uses a novel…
See paper details

Language Identification from Very Short Strings

Many language-related tasks, such as entering text on your iPhone, discovering news articles you might enjoy, or finding out answers to questions you may have, are powered by language-specific natural language processing (NLP) models. To decide which model to invoke at a particular point in time, we must perform language identification (LID), often on the basis of limited evidence, namely a short character string. Performing reliable LID is more critical than ever as multi-lingual input is becoming more and more common across all Apple platforms. In most writing scripts — like Latin and Cyrillic, but also including Hanzi, Arabic, and others — strings composed of a few characters are often present in more than one language, making reliable identification challenging. In this article, we explore how we can improve LID accuracy by treating it as a sequence labeling problem at the character level, and using bi-directional long short-term memory (bi-LSTM) neural networks trained on short character sequences. We observed reductions in error rates varying from 15% to 60%, depending on the language, while achieving reductions in model size between 40% and 80% compared to previously shipping solutions. Thus the LSTM LID approach helped us identify language more correctly in features such as QuickType keyboards and Smart Responses, thereby leading to better auto-corrections, completions, and predictions, and ultimately a more satisfying user experience. It also made public APIs like the Natural Language framework more robust to multi-lingual environments.

See highlight details