Niyati Bafna, Emily Chang, Nathaniel Romney Robinson, David R. Mortensen, Kenton Murray, David Yarowsky, Hale Sirin. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Atharva Naik, Darsh Agrawal, Hong Sng, Clayton Marr, Kexun Zhang, Nathaniel Romney Robinson, Kalvin Chang, Rebecca Byrnes, Aravind Mysore, Carolyn Rose, David R. Mortensen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Taiqi He, Kwanghee Choi, Lindia Tjuatja, Nathaniel Robinson, Jiatong Shi, Shinji Watanabe, Graham Neubig, David Mortensen, Lori Levin. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Mapping words into a fixed-dimensional vector space is the backbone of modern NLP. While most word embedding methods successfully encode semantic information, they overlook phonetic information that is crucial for many tasks. We develop three methods that use articulatory features to build phonetically informed word embeddings. To address the inconsistent evaluation of existing phonetic word embedding methods, we also contribute a task suite to fairly evaluate past, current, and future methods. We evaluate both (1) intrinsic aspects of phonetic word embeddings, such as word retrieval and correlation with sound similarity, and (2) extrinsic performance on tasks such as rhyme and cognate detection and sound analogies. We hope our task suite will promote reproducibility and inspire future phonetic embedding research.

Nathaniel Robinson, Raj Dabre, Ammon Shurtz, Rasul Dent, Onenamiyi Onesi, Claire Monroc, Loïc Grobol, Hasan Muhammad, Ashi Garg, Naome Etori, Vijay Murari Tiyyala, Olanrewaju Samuel, Matthew Stutzman, Bismarck Odoom, Sanjeev Khudanpur, Stephen Richardson, Kenton Murray. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Active learning is a powerful method for reducing the amount of labeled training data needed for a machine learning model to learn a task without degrading performance. This is accomplished by iteratively selecting the most informative samples from an unlabeled dataset to be labeled by an oracle (i.e., a human annotator) using an active learning sampling strategy. Pretrained models have been used in recent years as frontends for active learning neural networks to increase efficiency. This work applies active learning with pretrained models to the spoof detection task with the following two goals: 1) the identification of which pretrained speech models and active learning strategies are most effective for the spoof detection task, and 2) the development of an active learning method that selects the optimal sampling strategy from a list of available strategies at each step of the active learning process. This mixed strategy is shown to outperform all individual strategies for the task.

Developing Automatic Speech Recognition (ASR) for lowresource languages is a challenge due to the small amount of transcribed audio data.For many such languages, audio and text are available separately, but not audio with transcriptions.Using text, speech can be synthetically produced via text-tospeech (TTS) systems.However, many low-resource languages do not have quality TTS systems either.We propose an alternative: produce synthetic audio by running text from the target language through a trained TTS system for a higher-resource pivot language.We investigate when and how this technique is most effective in low-resource settings.In our experiments, using several thousand synthetic TTS text-speech pairs and duplicating authentic data to balance yields optimal results.Our findings suggest that searching over a set of candidate pivot languages can lead to marginal improvements and that, surprisingly, ASR performance can by harmed by increases in measured TTS quality.Application of these findings improves ASR by 64.5% and 45.0% character error reduction rate (CERR) respectively for two low-resource languages: Guaraní and Suba.

It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of learning to interface with a pretrained language model, just as Application Programming Interfaces (APIs) control the behavior of programs by altering hyperparameters. In this new paradigm, a specialized neural network (called a Neural Programming Interface or NPI) learns to interface with a pretrained language model by manipulating the hidden activations of the pretrained model to produce desired outputs. Importantly, no permanent changes are made to the weights of the original model, allowing us to re-purpose pretrained models for new tasks without overwriting any aspect of the language model. We also contribute a new data set construction algorithm and GAN-inspired loss function that allows us to train NPI models to control outputs of autoregressive transformers. In experiments against other state-of-the-art approaches, we demonstrate the efficacy of our methods using OpenAI’s GPT-2 model, successfully controlling noun selection, topic aversion, offensive speech filtering, and other aspects of language while largely maintaining the controlled model's fluency under deterministic settings.