5 research outputs found
The ADAPT system description for the STAPLE 2020 English-to-Portuguese translation task
This paper describes the ADAPT Centre’s submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task. In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences). We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g. data and sentence selection, monolingual MT for generating alternative translations, and combining multiple n-best translations. Our experiments show that adding the aforementioned techniques to the
baseline yields an excellent performance in the English-to-Portuguese translation task
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Taxonomies consist of machine-interpretable semantics and provide valuable
knowledge for many web applications. For example, online retailers (e.g.,
Amazon and eBay) use taxonomies for product recommendation, and web search
engines (e.g., Google and Bing) leverage taxonomies to enhance query
understanding. Enormous efforts have been made on constructing taxonomies
either manually or semi-automatically. However, with the fast-growing volume of
web content, existing taxonomies will become outdated and fail to capture
emerging knowledge. Therefore, in many applications, dynamic expansions of an
existing taxonomy are in great demand. In this paper, we study how to expand an
existing taxonomy by adding a set of new concepts. We propose a novel
self-supervised framework, named TaxoExpan, which automatically generates a set
of pairs from the existing taxonomy as training
data. Using such self-supervision data, TaxoExpan learns a model to predict
whether a query concept is the direct hyponym of an anchor concept. We develop
two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural
network that encodes the local structure of an anchor concept in the existing
taxonomy, and (2) a noise-robust training objective that enables the learned
model to be insensitive to the label noise in the self-supervision data.
Extensive experiments on three large-scale datasets from different domains
demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy
expansion.Comment: WWW 202