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8 papers · 2 filters
Building competitive direct acoustics-to-word models for English conversational speech recognition
Kartik Audhkhasi, Brian Kingsbury, Bhuvana Ramabhadran +2
Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional sub-word based automatic speech recognition models usin…
Kernelized Hashcode Representations for Relation Extraction
Sahil Garg, Aram Galstyan, Greg Ver Steeg +3
Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kern…
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
Jian Ni, Georgiana Dinu, Radu Florian
The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. H…
Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Jian Ni, Radu Florian
The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities…
Learning Structured Natural Language Representations for Semantic Parsing
Jianpeng Cheng, Siva Reddy, Vijay Saraswat +1
We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with…
Beam Search Strategies for Neural Machine Translation
Markus Freitag, Yaser Al-Onaizan
The basic concept in Neural Machine Translation (NMT) is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is then using a…