Despite their successes in NLP, Transformer-based language models still require extensive computing resources and suffer in low-resource or low-compute settings. In this paper, we present AxomiyaBERTa, a novel BERT model for Assamese, a morphologically-rich low-resource language (LRL) of Eastern India. AxomiyaBERTa is trained only on the masked language modeling (MLM) task, without the typical additional next sentence prediction (NSP) objective, and our results show that in resource-scarce settings for very low-resource languages like Assamese, MLM alone can be successfully leveraged for a range of tasks. AxomiyaBERTa achieves SOTA on token-level tasks like Named Entity Recognition and also performs well on “longer-context” tasks like Cloze-style QA and Wiki Title Prediction, with the assistance of a novel embedding disperser and phonological signals respectively. Moreover, we show that AxomiyaBERTa can leverage phonological signals for even more challenging tasks, such as a novel cross-document coreference task on a translated version of the ECB+ corpus, where we present a new SOTA result for an LRL. Our source code and evaluation scripts may be found at https://github.com/csu-signal/axomiyaberta.

Loanwords are words incorporated from one language into another without translation. Suppose two words from distantly-related or unrelated languages sound similar and have a similar meaning. In that case, this is evidence of likely borrowing. This paper presents a method to automatically detect loanwords across various language pairs, accounting for differences in script, pronunciation and phonetic transformation by the borrowing language. We incorporate edit distance, semantic similarity measures, and phonetic alignment. We evaluate on 12 language pairs and achieve performance comparable to or exceeding state of the art methods on single-pair loanword detection tasks. We also demonstrate that multilingual models perform the same or often better than models trained on single language pairs and can potentially generalize to unseen language pairs with sufficient data, and that our method can exceed human performance on loanword detection.

In this paper, we present an approach toward grounding linguistic positional and directional labels directly to human motions in the course of a disoriented balancing task in a multi-axis rotational device. We use deep neural models to predict human subjects’ joystick motions as well as the subjects’ proficiency in the task, combined with BERT embedding vectors for positional and directional labels extracted from annotations into an embodied direction classifier. We find that combining contextualized BERT embeddings with embeddings describing human motion and proficiency can successfully predict the direction a hypothetical human participant should move to achieve better balance with accuracy that is comparable to a moderately-proficient balancing task subject, and that our combined embodied model may actually make decisions that are objectively better than decisions made by some humans.