Research

We design loss functions for unsupervised text-compression that use auxiliary signals for text compression quality, such PLM-derived fluency and consistency with source inputs. The models outperform existing approaches that use discrete-search and are also very efficient at inference time due to a policy-based reinforcement learning training setup, which distills the ensemble of training targets into a single classification decision.

We propose a novel ensembling method for multi-input sequence to sequence models. This simple method leverages large pre-trained single document summarization models, and achieves SOTA performance on two multi-document summarization benchmarks

We present a new large-scale dataset for training and evaluating Multi-Document summarization systems in realistic settings.

We evaluate several methods of decoder parameter sharing for multilingual translation models, and present a means of evaluating performance on language pairs where no references are available. Ours was the only single-model multilingual system at WMT 2019.

In my PhD thesis, I created novel Computer-Aided Translation interface components, and developed new models for Machine Translation Quality Estimation and interactive Machine Translation.

Grid Beam Search (GBS) extends the beam search algorithm to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model which generates sequences token by token. Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate auxillary knowledge into a model’s output without requiring any modification of the parameters or training data.

Factored NMT models (models with multiple input representations) are used for both Automatic Post-Editing and Word-Level Translation Quality Estimation by tuning for the metrics on the respective task, and achieve good performance on both tasks.