While counterfactual examples are useful for analysis and training of NLP models, current
generation methods either rely on manual labor to create very few counterfactuals, or only …

Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence
techniques to automate an increasing range of tasks, especially those once considered the …

Sequence-to-sequence models are a powerful workhorse of NLP. Most variants employ a
softmax transformation in both their attention mechanism and output layer, leading to dense …

Automated decision support can accelerate tedious tasks as users can focus their attention
where it is needed most. However, a key concern is whether users overly trust or cede …

The profession of translation is undergoing enormous change, expedited by the global
recession that began in 2007–2008. Government policies and an intensive focus on cost …

Automatic text summarization systems commonly involve humans for preparing data or
evaluating model performance, yet, there lacks a systematic understanding of humans' roles …

Despite the promising results achieved in last years by statistical machine translation, and
more precisely, by the neural machine translation systems, this technology is still not error …

We present an interactive translation prediction method based on neural machine
translation. Even with the same translation quality of the underlying machine translation …

The goal of text simplification (TS) is to transform difficult text into a version that is easier to
understand and more broadly accessible to a wide variety of readers. In some domains …

We present a study on reinforcement learning (RL) from human bandit feedback for
sequence-to-sequence learning, exemplified by the task of bandit neural machine …