31 citations · 46 across the 4 of their papers we have counts for
9 papers
Investigating Failures of Automatic Translation in the Case of Unambiguous Gender
Adithya Renduchintala, Adina Williams
Transformer based models are the modern work horses for neural machine translation (NMT), reaching state of the art across several benchmarks. Despite their impressive accuracy, we…
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment
Ahmed El-Kishky, Adithya Renduchintala, James Cross +2
Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a la…
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li +3
Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process. While many algorithms have been proposed, there…
Quality Estimation without Human-labeled Data
Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala +3
Quality estimation aims to measure the quality of translated content without access to a reference translation. This is crucial for machine translation systems in real-world scenar…
A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation
Shuoyang Ding, Adithya Renduchintala, Kevin Duh
Most neural machine translation systems are built upon subword units extracted by methods such as Byte-Pair Encoding (BPE) or wordpiece. However, the choice of number of merge oper…
Pretraining by Backtranslation for End-to-end ASR in Low-Resource Settings
Matthew Wiesner, Adithya Renduchintala, Shinji Watanabe +3
We explore training attention-based encoder-decoder ASR in low-resource settings. These models perform poorly when trained on small amounts of transcribed speech, in part because t…