Anietie Andy

Anietie Andy, Siyi Liu, Daphne Ippolito et al.

While popular televised events such as presidential debates or TV shows are airing, people provide commentary on them in real-time. In this paper, we propose a simple yet effective approach to combine social media commentary and videos to create a multimedia summary of televised events. Our approach identifies scenes from these events based on spikes of mentions of people involved in the event and automatically selects tweets and frames from the videos that occur during the time period of the spike that talk about and show the people being discussed.

Garry Kuwanto, Eno-Abasi E. Urua, Priscilla Amondi Amuok et al.

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.

Desta Haileselassie Hagos, Legand L. Burge, Anietie Andy et al.

Transformer-based architectures have advanced text summarization, yet their quadratic complexity limits scalability on long documents. This paper introduces BiSparse-AAS (Bilinear Sparse Attention with Adaptive Spans), a novel framework that combines sparse attention, adaptive spans, and bilinear attention to address these limitations. Sparse attention reduces computational costs by focusing on the most relevant parts of the input, while adaptive spans dynamically adjust the attention ranges. Bilinear attention complements both by modeling complex token interactions within this refined context. BiSparse-AAS consistently outperforms state-of-the-art baselines in both extractive and abstractive summarization tasks, achieving average ROUGE improvements of about 68.1% on CNN/DailyMail and 52.6% on XSum, while maintaining strong performance on OpenWebText and Gigaword datasets. By addressing efficiency, scalability, and long-sequence modeling, BiSparse-AAS provides a unified, practical solution for real-world text summarization applications.

Yinhan Lu, Gaganpreet Jhajj, Chen Zhang et al.

In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent work on many-shot ICL suggests that modern LLMs can further benefit from larger ICL examples enabled by their long context windows. However, such gains depend on careful example selection, and the inference cost can be prohibitive for low-resource language communities. In this paper, we present an empirical study of many-shot ICL for machine translation from English into ten truly low-resource languages recently added to FLORES+. We analyze the effects of retrieving more informative examples, using out-of-domain data, and ordering examples by length. Our findings show that many-shot ICL becomes more effective as the number of examples increases. More importantly, we show that BM25-based retrieval substantially improves data efficiency: 50 retrieved examples roughly match 250 many-shot examples, while 250 retrieved examples perform similarly to 1,000 many-shot examples.

Anietie Andy, Sharath Guntuku

Individuals recovering from substance use often seek social support (emotional and informational) on online recovery forums, where they can both write and comment on posts, expressing their struggles and successes. A common challenge in these forums is that certain posts (some of which may be support seeking) receive no comments. In this work, we use data from two Reddit substance recovery forums:/r/Leaves and/r/OpiatesRecovery, to determine the relationship between the social supports expressed in the titles of posts and the number of comments they receive. We show that the types of social support expressed in post titles that elicit comments vary from one substance use recovery forum to the other.