We introduce Omnilingual MT, a family of multilingual translation (MT) systems covering more than 1,600 languages—the broadest benchmarked coverage of any MT system to date. We make contributions in several key research questions in the field: strategies for the use of monolingual data, curating seed training data, tokenization and suitable architectures, the competitiveness of small specialized MT models as opposed to general purpose large models, and evaluation protocols. Our data strategy combines large-scale multilingual corpora with backtranslation for 2,000+ languages and MeDLEy, a grammatically-diverse seed dataset for 109 low-resource languages. We explore two complementary architectures: OMT-LLAMA, a decoder-only model (1B/3B/8B) built on LLAMA3 with multilingual continual pretraining, and OMT-NLLB, a 3B encoder-decoder model with a novel three-stage training strategy exploiting non-parallel data via autoencoding. For evaluation, we extend the BOUQuET benchmark to 275 languages, develop Met-BOUQuET with human annotations of machine quality in 161 directions, and BLASER 3, a reference-free quality estimation metric. Our specialized 1B–8B models match or exceed 70B general-purpose LLMs, demonstrating that specialization is a more reliable path to multilingual translation than scale alone. Omnilingual MT achieves “passable” translation quality as judged by human annotators for over 440 languages, doubling it over the previous state of the field at ∼200 languages, and translates in zero-shot mode into ∼1,200 languages versus 300–400 for the baselines. With Omnilingual MT, we showcase a path encompassing the full pipeline of data and system development towards designing models that are multilingual from the onset.

Text-based environments enable RL agents to learn to converse and perform interactive tasks through natural language. However, previous RL approaches applied to text-based environments show poor performance when evaluated on unseen games. This paper investigates the improvement of generalisation performance through the simple switch from a value-based update method to a policy-based one, within text-based environments. We show that by replacing commonly used value-based methods with REINFORCE with baseline, a far more general agent is produced. The policy-based agent is evaluated on Coin Collector and Question Answering with interactive text (QAit), two text-based environments designed to test zero-shot performance. We see substantial improvements on a variety of zero-shot evaluation experiments, including tripling accuracy on various QAit benchmark configurations. The results indicate that policy-based RL has significantly better generalisation capabilities than value-based methods within such text-based environments, suggesting that RL agents could be applied to more complex natural language environments.

Interactive Question Answering (IQA) requires an intelligent agent to interact with a dynamic environment in order to gather information necessary to answer a question. IQA tasks have been proposed as means of training systems to develop language or visual comprehension abilities. To this end, the Question Answering with Interactive Text (QAit) task was created to produce and benchmark interactive agents capable of seeking information and answering questions in unseen environments. While prior work has exclusively focused on IQA as a reinforcement learning problem, such methods suffer from low sample efficiency and poor accuracy in zero-shot evaluation. In this paper, we propose the use of the recently proposed Decision Transformer architecture to provide improvements upon prior baselines. By utilising a causally masked GPT-2 Transformer for command generation and a BERT model for question answer prediction, we show that the Decision Transformer achieves performance greater than or equal to current state-of-the-art RL baselines on the QAit task in a sample efficient manner. In addition, these results are achievable by training on sub-optimal random trajectories, therefore not requiring the use of online agents to gather data.

We present RepGraph, an open source visualisation and analysis tool for meaning representation graphs. Graph-based meaning representations provide rich semantic annotations, but visualising them clearly is more challenging than for fully lexicalized representations. Our application provides a seamless, unifying interface with which to visualise, manipulate and analyse semantically parsed graph data represented in a JSON-based serialisation format. RepGraph visualises graphs in multiple formats, with an emphasis on showing the relation between nodes and their corresponding token spans, whilst keeping the representation compact. Additionally, the web-based tool provides NLP researchers with a clear, visually intuitive way of interacting with these graphs, and includes a number of graph analysis features. The tool currently supports the DMRS, EDS, PTG, UCCA, and AMR semantic frameworks. A live demo is available at https://repgraph.vercel.app/.