Findings of the Fourth Workshop on Neural Generation and Translation (2020)

Abstract: We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.

## introduction neural sequence to sequence models @xcite @xcite are the workhorse behind a wide variety of different natural language processing tasks such as machine translation, generation, summarization and simplification. the 4th workshop on neural machine translation and generation (wngt 2020) provided a forum for research in applications of neural models to machine translation and other language generation tasks (including summarization, nlg from structured data, dialog response generation, among others). overall, the workshop was held with two goals. first, it aimed to synthesize the current state of knowledge in neural machine translation and generation: this year we continued to encourage submissions that not only advance the state of the art through algorithmic advances, but also analyze and understand the current state of the art, pointing to future research directions. towards this goal, we received a number of high-quality research contributions on the workshop topics, as summarized in section 2. second, the workshop aimed to expand the research horizons in nmt: we continued to organize the efficient nmt task which encouraged participants to develop not only accurate but computationally efficient systems. this task had three participants each with a number of individual systems. we organized the second shared task on "document-level generation and translation", which aims to push forward document-level generation technology and contrast the methods for different types of inputs. unfortunately this task only had one participant. finally, we introduced a new shared task, organised by duolingo, which encouraged models to produce as many correct translations as possible for a given input. this task generated a lot of interest and there were 11 participants. the results of the shared task are summarized in sections 3, 4 and 5. ## summary of research contributions similar to last year we invited the mt and nlg community to contribute to the workshop with long papers, extended abstracts for preliminary work, and cross-submissions of papers that have appeared in other venues. keeping up with with the main vision of the workshop, we were aiming for a variety of works at the intersection of machine translation and language generation tasks. we received a total of 28 submissions, from which we accepted 16. there were 2 crosssubmissions, 3 extended abstracts and 11 full papers. there were also 15 system submission papers. we elicted two double-blind reviews for each submission, avoiding conflicts of interest. with regards to thematology there were 8 papers with a focus on natural language generation and 8 with the application of machine translation in mind. the underlying emphasis across submissions was placed this year on capitalizing on the use of pre-training models (e.g., bert; @xcite especially for low-resource datasets. the quality of the accepted publications was very high; there was a significant drop in numbers though in comparison to last year (36 accepted papers from 68 submissions) which is most likely due to the extra overhead on conducting research under lockdown policies sanctioned globally due to covid-19 pandemic.