Downstream processing of machine translation (MT) output promises to be a solution to improve translation quality, especially when the MT system’s internal decoding process is n...
Rajen Chatterjee, Marion Weller, Matteo Negri, Mar...
We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose ...
Hendra Setiawan, Zhongqiang Huang, Jacob Devlin, T...
Semantic applications typically extract information from intermediate structures derived from sentences, such as dependency parse or semantic role labeling. In this paper, we stud...
We present cort, a modular toolkit for devising, implementing, comparing and analyzing approaches to coreference resolution. The toolkit allows for a unified representation of po...
Sebastian Martschat, Patrick Claus, Michael Strube...
This paper introduces a web-based visualization framework for graph-based distributional semantic models. The visualization supports a wide range of data structures, including ter...
Eugen Ruppert, Manuel Kaufmann, Martin Riedl 0002,...
We present a new factoid-annotated dataset for evaluating content models for scientific survey article generation containing 3,425 sentences from 7 topics in natural language pro...
Rahul Jha, Catherine Finegan-Dollak, Ben King, Ree...
Mention pair models that predict whether or not two mentions are coreferent have historically been very effective for coreference resolution, but do not make use of entity-level i...
We propose an event-driven model for headline generation. Given an input document, the system identifies a key event chain by extracting a set of structural events that describe ...
Contrasting meaning is a basic aspect of semantics. Recent word-embedding models based on distributional semantics hypothesis are known to be weak for modeling lexical contrast. W...