4 citations · 8 across the 6 of their papers we have counts for
8 papers
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains
Navid Rekabsaz, Nikolaos Pappas, James Henderson +2
Neural language modeling (LM) has led to significant improvements in several applications, including Automatic Speech Recognition. However, they typically require large amounts of…
Deep Residual Output Layers for Neural Language Generation
Nikolaos Pappas, James Henderson
Many tasks, including language generation, benefit from learning the structure of the output space, particularly when the space of output labels is large and the data is sparse. St…
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings
Haozhou Wang, James Henderson, Paola Merlo
Distributed representations of words which map each word to a continuous vector have proven useful in capturing important linguistic information not only in a single language but a…
Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics
James Henderson
Lexical entailment, such as hyponymy, is a fundamental issue in the semantics of natural language. This paper proposes distributional semantic models which efficiently learn word e…
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction
Diana Nicoleta Popa, James Henderson
Vector-space models, from word embeddings to neural network parsers, have many advantages for NLP. But how to generalise from fixed-length word vectors to a vector space for arbitr…
A Vector Space for Distributional Semantics for Entailment
James Henderson, Diana Nicoleta Popa
Distributional semantics creates vector-space representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We prop…