What set of semantic tags did they use?
Tags categories ranging from anaphoric (definite, possessive pronoun), attribute (colour, concrete quantity, intersective, relation), unnamed entity (concept), logical (alternative, disjunction), discourse (subordinate relation), events (present simple, past simple), etc.
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.
Multi-task learning (MTL) is a recently resurgent approach to machine learning in which multiple tasks are simultaneously learned. By optimising the multiple loss functions of related tasks at once, multi-task learning models can achieve superior results compared to models trained on a single task. The key principle is summarized by BIBREF0 as “MTL improves generalization by leveraging the domain-specific information contained in the training signals of related tasks". Neural MTL has become an increasingly successful approach by exploiting similarities between Natural Language Processing (NLP) tasks BIBREF1 , BIBREF2 , BIBREF3 . Our work builds upon BIBREF4 , who demonstrate that employing semantic tagging as an auxiliary task for Universal Dependency BIBREF5 part-of-speech tagging can lead to improved performance. The objective of this paper is to investigate whether learning to predict lexical semantic categories can be beneficial to other NLP tasks. To achieve this we augment single-task models (ST) with an additional classifier to predict semantic tags and jointly optimize for both the original task and the auxiliary semantic tagging task. Our hypothesis is that learning to predict semantic tags as an auxiliary task can improve performance of single-task systems. We believe that this is, among other factors, due to the following: We test our hypothesis on three disparate NLP tasks: (i) Universal Dependency part-of-speech tagging (UPOS), (ii) Universal Dependency parsing (UD DEP), a complex syntactic task; and (iii) Natural Language Inference (NLI), a complex task requiring deep natural language understanding.