250 citations · 394 across the 20 of their papers we have counts for
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How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?
Ali Araabi, Christof Monz, Vlad Niculae
Neural Machine Translation (NMT) is an open vocabulary problem. As a result, dealing with the words not occurring during training (a.k.a. out-of-vocabulary (OOV) words) have long b…
Optimizing Transformer for Low-Resource Neural Machine Translation
Ali Araabi, Christof Monz
Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. While the Transformer model has achie…
The Unreasonable Volatility of Neural Machine Translation Models
Marzieh Fadaee, Christof Monz
Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unan…
TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance Generation
Shaojie Jiang, Thomas Wolf, Christof Monz +1
Natural Language Generation (NLG) models are prone to generating repetitive utterances. In this work, we study the repetition problem for encoder-decoder models, using both recurre…
Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation
Jiahuan Pei, Pengjie Ren, Christof Monz +1
Dialogue response generation (DRG) is a critical component of task-oriented dialogue systems (TDSs). Its purpose is to generate proper natural language responses given some context…
BERT for Evidence Retrieval and Claim Verification
Amir Soleimani, Christof Monz, Marcel Worring
Motivated by the promising performance of pre-trained language models, we investigate BERT in an evidence retrieval and claim verification pipeline for the FEVER fact extraction an…