8 citations · 16 across the 3 of their papers we have counts for
6 papers · 1 filter
Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses
Prathyusha Jwalapuram, Shafiq Joty, Youlin Shen
Popular Neural Machine Translation model training uses strategies like backtranslation to improve BLEU scores, requiring large amounts of additional data and training. We introduce…
Can Your Context-Aware MT System Pass the DiP Benchmark Tests? : Evaluation Benchmarks for Discourse Phenomena in Machine Translation
Prathyusha Jwalapuram, Barbara Rychalska, Shafiq Joty +1
Despite increasing instances of machine translation (MT) systems including contextual information, the evidence for translation quality improvement is sparse, especially for discou…
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks
Tasnim Mohiuddin, Prathyusha Jwalapuram, Xiang Lin +1
Although coherence modeling has come a long way in developing novel models, their evaluation on downstream applications for which they are purportedly developed has largely been ne…
Zero-Resource Cross-Lingual Named Entity Recognition
M Saiful Bari, Shafiq Joty, Prathyusha Jwalapuram
Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. Ho…
Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite
Prathyusha Jwalapuram, Shafiq Joty, Irina Temnikova +1
The ongoing neural revolution in machine translation has made it easier to model larger contexts beyond the sentence-level, which can potentially help resolve some discourse-level…
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing
Xiang Lin, Shafiq Joty, Prathyusha Jwalapuram +1
We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter t…