Ngram-based statistical machine translation enhanced with multiple weighted reordering hypotheses

This paper describes the 2007 Ngram-based sta-tistical machine translation system developed at the TALP Research Center of the UPC (Uni-versitat Politecnica de Catalunya) in Barcelona. Emphasis is put on improvements and extensions of the previous years system, being highlighted and empirically compared. Mainly, these include a novel word ordering strategy based on: (1) sta-tistically monotonizing the training source cor-pus and (2) a novel reordering approach based on weighted reordering graphs. In addition, this system introduces a target language model based on statistical classes, a feature for out-of-domain units and an improved optimization procedure. The paper provides details of this system par-ticipation in the ACL 2007 SECOND WORK-SHOP ON STATISTICAL MACHINE TRANSLA-TION. Results on three pairs of languages are reported, namely from Spanish, French and Ger-man into English (and the other way round) for both the in-domain and out-of-domain tasks.

AI reflections in 2020

We invited authors of selected Comments and Perspectives published in Nature Machine Intelligence in the latter half of 2019 and first half of 2020 to describe how their topic has developed, what their thoughts are about the challenges of 2020, and what they look forward to in 2021