Lucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, Madian Khabsa. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Large multilingual language models typically rely on a single vocabulary shared across 100+ languages.As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged.This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R.In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language.Tokenizations using our vocabulary are typically more semantically meaningful and shorter compared to XLM-R.Leveraging this improved vocabulary, we train XLM-V, a multilingual language model with a one million token vocabulary.XLM-V outperforms XLM-R on every task we tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), to named entity recognition (WikiAnn).XLM-V is particularly effective on low-resource language tasks and outperforms XLM-R by 11.2% and 5.8% absolute on MasakhaNER and Americas NLI, respectively.

Pretrained masked language models (MLMs) require finetuning for most NLP tasks.Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one.We show that PLLs outperform scores from autoregressive language models like GPT-2 in a variety of tasks.By rescoring ASR and NMT hypotheses, RoBERTa reduces an endto-end LibriSpeech model's WER by 30% relative and adds up to +1.7 BLEU on state-of-theart baselines for low-resource translation pairs, with further gains from domain adaptation.We attribute this success to PLL's unsupervised expression of linguistic acceptability without a left-to-right bias, greatly improving on scores from GPT-2 (+10 points on island effects, NPI licensing in BLiMP).One can finetune MLMs to give scores without masking, enabling computation in a single inference pass.In all, PLLs and their associated pseudo-perplexities (PP-PLs) enable plug-and-play use of the growing number of pretrained MLMs; e.g., we use a single cross-lingual model to rescore translations in multiple languages.We release our library for language model scoring at https: //github.com/awslabs/mlm-scoring.

Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an otherwise state-of-the-art model. To harden models against background noise, practitioners often perform data augmentation, adding artificially-noised examples to the training set, carrying over the original label. In this paper, we hypothesize that a clean example and its superficially perturbed counterparts shouldn't merely map to the same class - they should map to the same representation. We propose invariant-representation-learning (IRL): At each training iteration, for each training example, we sample a noisy counterpart. We then apply a penalty term to coerce matched representations at each layer (above some chosen layer). Our key results, demonstrated on the LibriSpeech dataset are the following: (i) IRL significantly reduces character error rates (CER) on both `clean' (3.3% vs 6.5%) and `other' (11.0% vs 18.1%) test sets; (ii) on several-of-domain noise settings (different from those seen during training) IRL's benefits are even more pronounced. Careful ablations confirm that our results are not simply due to shrinking activations at the chosen layers.