We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages for morphological annotation, POS-tagging, lemmatization, character- and word-level gap-filling. We developed a simple, uniform, and computationally lightweight approach based on the adapters framework using parameter-efficient fine-tuning. We applied the same adapter-based approach uniformly to all tasks and 16 languages by fine-tuning stacked language- and task-specific adapters. Our submission obtained an overall second place out of three submissions, with the first place in word-level gap-filling. Our results show the feasibility of adapting language models pre-trained on modern languages to historical and ancient languages via adapter training.

NLP methods can aid historians in analyzing textual materials in greater volumes than manually feasible. Developing such methods poses substantial challenges though. First, acquiring large, annotated historical datasets is difficult, as only domain experts can reliably label them. Second, most available off-the-shelf NLP models are trained on modern language texts, rendering them significantly less effective when applied to historical corpora. This is particularly problematic for less well studied tasks, and for languages other than English. This paper addresses these challenges while focusing on the under-explored task of event extraction from a novel domain of historical texts. We introduce a new multilingual dataset in English, French, and Dutch composed of newspaper ads from the early modern colonial period reporting on enslaved people who liberated themselves from enslavement. We find that: 1) even with scarce annotated data, it is possible to achieve surprisingly good results by formulating the problem as an extractive QA task and leveraging existing datasets and models for modern languages; and 2) cross-lingual low-resource learning for historical languages is highly challenging, and machine translation of the historical datasets to the considered target languages is, in practice, often the best-performing solution.

Let's face it - the meteoric rise in digital and text communication has drastically changed the way we speak to one another. This ever-evolving shift in language creates a massive burden for ediscovery teams, who need to understand how text is used in context in order to effectively use legal technology to navigate massive amounts of data. In this episode, Amanda Jones of Lighthouse joins Bill and Rob to illuminate some common challenges and pitfalls that can arise with modern language in ediscovery. Let's face it - the meteoric rise in digital and text communication has drastically changed the way we speak to one another. This ever-evolving shift in language creates a massive burden for ediscovery teams, who need to understand how text is used in context in order to effectively use legal technology to navigate massive amounts of data.

Many ML studies have introduced systems for deciphering and translating ancient texts into modern language, and these have proven useful to history, archaeology and digital humanities scholars. Now, researchers from the University of Sheffield, Beihang University, and Open University's Knowledge Media Institute have proposed a transfer learning approach that can automatically process historical texts at a semantic level to generate modern language summaries. The method outperforms standard cross-lingual benchmarks on the task. Historical text summarization can be regarded as a unique form of cross-lingual summarization. Progress in traditional cross-lingual summarization has however been hindered by limited historical and modern language corpora and evolving vocabulary, spelling, meanings and grammar.

Researchers now believe that language might have developed from cave drawings. Specifically, they think the ancient artwork located in caves with good acoustics might have inspired humans to develop the vocal communication that exists today. A new report on the theory from the Massachusetts Institute of Technology hinges on the fact that caves allow for echoes. The researchers think that previously discovered cave drawings prove that the fundamental parts of speech were derived from artwork. The MIT team that worked on the new analysis said that taking a look in the depths of ancient caves can provide modern humans with answers about where languages stemmed from.

When Watson answered the final question to win "Jeopardy!" in 2011, voice recognition and artificial intelligence software were just making their large consumer debut on mobile devices. At the start, these capabilities were engaging, interesting and even exciting, but sometimes more as a parlor game than a deeply functional application. Yet they've continued to improve at an accelerating rate and now demand our serious attention as productivity tools. We are now half a decade on, and cognitive computing is making its presence felt at a deeply functional business level -- not just at the gateway of the journey on our devices, but deep within the industry-process level. For instance, cognitive computing is having a real impact at the clinical-process level for healthcare, which was featured specifically in a segment on artificial intelligence on "60 Minutes."

It's a question you might never have considered before – why do older siblings do better on IQ tests than their younger counterparts? But if you want to get into Oxford's experimental psychology program, you'd better be prepared to answer. The university has released a series of questions from tutors who conduct the infamous interviews, revealing the complex problems in everything from mathematics to medicine used to spot the sharpest candidates. Oxford has released a series of questions from tutors who conduct the infamous interviews, revealing the complex problems in everything from mathematics to medicine used to spot the sharpest candidates. Oxford has revealed five interview questions spanning Modern Languages, Medicine, Philosophy, Maths, and Experimental Psychology.