Question generation is an important yet challenging problem in Artificial Intelligence (AI),
which aims to generate natural and relevant questions from various input formats, eg …

We present YiSi, a unified automatic semantic machine translation quality evaluation and
estimation metric for languages with different levels of available resources. Underneath the …

Transforming unstructured text into a formal representation is an important goal of the
Semantic Web in order to facilitate the integration and retrieval of information. The …

This paper introduces a novel model for semantic role labeling that makes use of neural
sequence modeling techniques. Our approach is motivated by the observation that complex …

Compositional embedding models build a representation (or embedding) for a linguistic
structure based on its component word embeddings. We propose a Feature-rich …

We present a new, efficient frame-semantic parser that labels semantic arguments to
FrameNet predicates. Built using an extension to the segmental RNN that emphasizes …

We introduce a simple and accurate neural model for dependency-based semantic role
labeling. Our model predicts predicate-argument dependencies relying on states of a …

We present a new method for semantic role labeling in which arguments and semantic roles
are jointly embedded in a shared vector space for a given predicate. These embeddings …

Lexical embeddings can serve as useful representations for words for a variety of NLP tasks,
but learning embeddings for phrases can be challenging. While separate embeddings are …

We describe a new version of MEANT, which participated in the metrics task of the Second
Conference on Machine Translation (WMT 2017). MEANT 2.0 uses idfweighted …