Due to computational constraints, we leave this as the subject for future work.

Literary translation requires preserving cultural nuances and stylistic elements, which traditional metrics like BLEU and METEOR fail to assess due to their focus on lexical overlap. This oversight neglects the narrative consistency and stylistic fidelity that are crucial for literary works. To address this, we propose MAS-LitEval, a multi-agent system using Large Language Models (LLMs) to evaluate translations based on terminology, narrative, and style. We tested MAS-LitEval on translations of The Little Prince and A Connecticut Yankee in King Arthur's Court, generated by various LLMs, and compared it to traditional metrics. \textbf{MAS-LitEval} outperformed these metrics, with top models scoring up to 0.890 in capturing literary nuances. This work introduces a scalable, nuanced framework for Translation Quality Assessment (TQA), offering a practical tool for translators and researchers.

News that Dutch publishing house Veen Bosch & Keuning (VBK) has confirmed plans to experiment using AI to translate fiction has stirred up a thought-provoking debate. Some believe it marks the beginning of the end for human translators, while others see this as the opening up of a new world of possibilities to bring more literature to even more people. These arguments are becoming increasingly vocal as the advance of AI accelerates at an ever-increasing rate. This debate interests me as my work examines the intersections of art, ethics, technology and culture, and I have published research in areas of emerging technologies, particularly in relation to human enhancement. Across every new technology, debate centres on what we stand to lose by embracing change and, with AI, this echoes the developments in the recent history of genetic science.

Sentiment analysis (SA) plays a vital role in Natural Language Processing (NLP) by ~identifying sentiments expressed in text. Although significant advances have been made in SA for widely spoken languages, low-resource languages such as Hausa face unique challenges, primarily due to a lack of digital resources. This study investigates the effectiveness of Language-Adaptive Fine-Tuning (LAFT) to improve SA performance in Hausa. We first curate a diverse, unlabeled corpus to expand the model's linguistic capabilities, followed by applying LAFT to adapt AfriBERTa specifically to the nuances of the Hausa language. The adapted model is then fine-tuned on the labeled NaijaSenti sentiment dataset to evaluate its performance. Our findings demonstrate that LAFT gives modest improvements, which may be attributed to the use of formal Hausa text rather than informal social media data. Nevertheless, the pre-trained AfriBERTa model significantly outperformed models not specifically trained on Hausa, highlighting the importance of using pre-trained models in low-resource contexts. This research emphasizes the necessity for diverse data sources to advance NLP applications for low-resource African languages. We published the code and the dataset to encourage further research and facilitate reproducibility in low-resource NLP here: https://github.com/Sani-Abdullahi-Sani/Natural-Language-Processing/blob/main/Sentiment%20Analysis%20for%20Low%20Resource%20African%20Languages

This study sets out to answer one major question: Can ChatGPT capture swearing nuances? It presents an empirical study on the ability of ChatGPT to translate Arabic oath expressions into English. 30 Arabic oath expressions were collected from the literature. These 30 oaths were first translated via ChatGPT and then analyzed and compared to the human translation in terms of types of gaps left unfulfilled by ChatGPT. Specifically, the gaps involved are: religious gap, cultural gap, both religious and cultural gaps, no gap, using non-oath particles, redundancy and noncapturing of Arabic script diacritics. It concludes that ChatGPT translation of oaths is still much unsatisfactory, unveiling the need of further developments of ChatGPT, and the inclusion of Arabic data on which ChatGPT should be trained including oath expressions, oath nuances, rituals, and practices.

As anyone who has tried pointing their phone's camera at a menu in a foreign country lately will know, machine translation has improved rapidly since the first days of Google Translate. The utility of AI-powered translation in situations like this is unquestionable – but the proposed use of AI in literary translation has been significantly more controversial. Dutch publisher Veen Bosch & Keuning's announcement that it would use AI translation for commercial fiction has outraged both authors and translators – despite attempts to reassure them with promises that no books will be translated in this way without careful checking and that authors will have to give consent. "A translator translates more than just words, we build bridges between cultures, taking into account the target readership every step of the way," says Michele Hutchison, winner of 2020's International Booker prize for her translation of Lucas Rijneveld's The Discomfort of Evening. "We smuggle in subtle clues to help the reader understand particular cultural elements or traditions. We convey rhythm, poetry, wordplay, metaphor. We research the precise terminology for say agricultural machinery, even in a novel."

Foley sound synthesis is crucial for multimedia production, enhancing user experience by synchronizing audio and video both temporally and semantically. Recent studies on automating this labor-intensive process through video-to-sound generation face significant challenges. Systems lacking explicit temporal features suffer from poor controllability and alignment, while timestamp-based models require costly and subjective human annotation. We propose Video-Foley, a video-to-sound system using Root Mean Square (RMS) as a temporal event condition with semantic timbre prompts (audio or text). RMS, a frame-level intensity envelope feature closely related to audio semantics, ensures high controllability and synchronization. The annotation-free self-supervised learning framework consists of two stages, Video2RMS and RMS2Sound, incorporating novel ideas including RMS discretization and RMS-ControlNet with a pretrained text-to-audio model. Our extensive evaluation shows that Video-Foley achieves state-of-the-art performance in audio-visual alignment and controllability for sound timing, intensity, timbre, and nuance. Code, model weights, and demonstrations are available on the accompanying website. (https://jnwnlee.github.io/video-foley-demo)

This paper presents a system for diversity-aware autonomous conversation leveraging the capabilities of large language models (LLMs). The system adapts to diverse populations and individuals, considering factors like background, personality, age, gender, and culture. The conversation flow is guided by the structure of the system's pre-established knowledge base, while LLMs are tasked with various functions, including generating diversity-aware sentences. Achieving diversity-awareness involves providing carefully crafted prompts to the models, incorporating comprehensive information about users, conversation history, contextual details, and specific guidelines. To assess the system's performance, we conducted both controlled and real-world experiments, measuring a wide range of performance indicators.

In Spanish, there are at least a dozen ways to say someone has the flu -- depending on the country. Translating "cardiac arrest" into Spanish is also tricky because "arresto" means getting detained by the police. Likewise, "intoxicado" means you have food poisoning, not that you're drunk. The examples of how translation could go awry in any language are endless: Words take on new meanings, idioms come and go, and communities adopt slang and dialects for everyday life. Human translators work hard to keep up with the changes, but California plans to soon entrust that responsibility to technology. State health policy officials want to harness emerging artificial intelligence technology to translate a broad swath of documents and websites related to "health and social services information, programs, benefits and services," according to state records.