10 citations · 12 across the 4 of their papers we have counts for
9 papers · 1 filter
Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions
Cuong Hoang, Devendra Sachan, Prashant Mathur +2
Several recent studies have reported dramatic performance improvements in neural machine translation (NMT) by augmenting translation at inference time with fuzzy-matches retrieved…
Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions
Cuong Hoang, Devendra Sachan, Prashant Mathur +2
We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. We build on the id…
Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric
Giorgos Vernikos, Brian Thompson, Prashant Mathur +1
We hypothesize that existing sentence-level machine translation (MT) metrics become less effective when the human reference contains ambiguities. To verify this hypothesis, we pres…
Improving Arabic Diacritization by Learning to Diacritize and Translate
Brian Thompson, Ali Alshehri
We propose a novel multitask learning method for diacritization which trains a model to both diacritize and translate. Our method addresses data sparsity by exploiting large, readi…
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity
Brian Thompson, Matt Post
Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompso…
Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing
Brian Thompson, Matt Post
We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propo…