Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Prompting has recently been shown as a promising approach for applying pre-trained
language models to perform downstream tasks. We present Multi-Stage Prompting (MSP), a …

Neural machine translation (NMT) has obtained significant performance improvement over
the recent years. However, NMT models still face various challenges including fragility and …

Multimodal machine translation and textual chat translation have received considerable
attention in recent years. Although the conversation in its natural form is usually multimodal …

$ k $ NN-MT is a straightforward yet powerful approach for fast domain adaptation, which
directly plugs pre-trained neural machine translation (NMT) models with domain-specific …

Neural chat translation aims to translate bilingual conversational text, which has a broad
application in international exchanges and cooperation. Despite the impressive …

Process-oriented data analysis techniques allow organizations to understand how their
processes operate, where modifications are needed and where enhancements are possible …

Recently, multi-aspect controllable text generation that controls the generated text in multiple
aspects (eg, sentiment, topic, and keywords) has attracted increasing attention. Although …

Fine-grained entity typing (FGET) aims to classify named entity mentions into fine-grained
entity types, which is meaningful for entity-related NLP tasks. For FGET, a key challenge is …

Neural Chat Translation (NCT) aims to translate conversational text between speakers of
different languages. Despite the promising performance of sentence-level and context …