23 citations · 43 across the 7 of their papers we have counts for
7 papers
Corpora Generation for Grammatical Error Correction
Jared Lichtarge, Chris Alberti, Shankar Kumar +3
Grammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, G…
Neural Language Modeling with Visual Features
Antonios Anastasopoulos, Shankar Kumar, Hank Liao
Multimodal language models attempt to incorporate non-linguistic features for the language modeling task. In this work, we extend a standard recurrent neural network (RNN) language…
Weakly Supervised Grammatical Error Correction using Iterative Decoding
Jared Lichtarge, Christopher Alberti, Shankar Kumar +2
We describe an approach to Grammatical Error Correction (GEC) that is effective at making use of models trained on large amounts of weakly supervised bitext. We train the Transform…
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
Tara N. Sainath, Rohit Prabhavalkar, Shankar Kumar +9
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-…
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition
Shankar Kumar, Michael Nirschl, Daniel Holtmann-Rice +3
Recurrent neural network (RNN) language models (LMs) and Long Short Term Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform traditional N-gram LMs on speech rec…
NN-grams: Unifying neural network and n-gram language models for Speech Recognition
Babak Damavandi, Shankar Kumar, Noam Shazeer +1
We present NN-grams, a novel, hybrid language model integrating n-grams and neural networks (NN) for speech recognition. The model takes as input both word histories as well as n-g…