November 7, 2021 By: Shuo Sun, Ahmed El-Kishky, Vishrav Chaudhary, James Cross, Francisco Guzmán, Lucia Specia Abstract Sentence-level Quality Estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on large multilingual contextualized language models that are computationally expensive and thus infeasible for many real-world applications. In this […]

Abstract Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines). This task has been previously instantiated as a form of textual entailment, which results in poor accuracy due to the complexity of the policies. In this paper we propose to address policy compliance detection via decomposing it into question answering, where questions check whether […]

Abstract Recent results in end-to-end automatic speech recognition have demonstrated the efficacy of pseudo-labeling for semi-supervised models trained both with Connectionist Temporal Classification (CTC) and Sequence-to-Sequence (seq2seq) losses. Iterative Pseudo-Labeling (IPL), which continuously trains a single model using pseudo-labels iteratively re-generated as the model learns, has been shown to further improve performance in ASR. We improve upon the IPL algorithm: as the model learns, we propose to iteratively re-generate transcriptions with hard labels (the most probable tokens), that is, without […]

August 30, 2021 By: Ju Lin, Yun Wang, Kaustubh Kalgaonkar, Gil Keren, Didi Zhang, Christian Fuegen Abstract Algorithms for speech bandwidth extension (BWE) may work in either the time domain or the frequency domain. Time-domain methods often do not sufficiently recover the high-frequency content of speech signals; frequency-domain methods are better at recovering the spectral envelope, but have difficulty reconstructing the details of the waveform. In this paper, we propose a two-stage approach for BWE, which enjoys the advantages of […]

August 30, 2021 By: Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi, Xuankai Chang, Daniel Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Godic Lee, Darong Liu, Zili Huang, Annie Dong, Shang-Wen Li, Shinji Watanabe, Abdelrahman Mohamed, Hung-yi Lee Abstract Using self-supervised learning methods to pre-train a network on large volumes of unlabeled data followed by fine-tuning for multiple downstream tasks has proven vital for advancing research in natural language representation learning. However, […]

July 23, 2021 By: Danielle Rothermel, Margaret Li, Tim Rocktäschel, Jakob Foerster Abstract Self-supervised pre-training of large-scale transformer models on text corpora followed by fine-tuning has achieved state-of-the-art on a number of natural language processing tasks. Recently, Lu et al. (2021) claimed that frozen pretrained transformers (FPTs) match or outperform training from scratch as well as unfrozen (fine-tuned) pretrained transformers in a set of transfer tasks to other modalities. In our work, we find that this result is, in fact, […]

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July 31, 2021 By: Aitor Ormazabal, Mikel Artetxe, Aitor Soroa, Gorka Labaka, Eneko Agirre Abstract Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this assumption. In this paper, we propose an alternative approach that does not have this limitation, while requiring a weak seed dictionary […]

June 10, 2021 By: Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, Adina Williams Abstract We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop dataset creation: annotators seek to create examples that a target model will misclassify, but that […]