187 citations · 698 across the 55 of their papers we have counts for
14 papers · 2 filters
On Compositionality in Neural Machine Translation
Vikas Raunak, Vaibhav Kumar, Florian Metze
We investigate two specific manifestations of compositionality in Neural Machine Translation (NMT) : (1) Productivity - the ability of the model to extend its predictions beyond th…
Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models
Siddharth Dalmia, Abdelrahman Mohamed, Mike Lewis +2
Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2…
Multitask Learning For Different Subword Segmentations In Neural Machine Translation
Tejas Srinivasan, Ramon Sanabria, Florian Metze
In Neural Machine Translation (NMT) the usage of subwords and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words.…
On Leveraging the Visual Modality for Neural Machine Translation
Vikas Raunak, Sang Keun Choe, Quanyang Lu +2
Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics. Recently, Caglayan et al. posit that the obser…
On Dimensional Linguistic Properties of the Word Embedding Space
Vikas Raunak, Vaibhav Kumar, Vivek Gupta +1
Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties. In this work, we analyze word embedding…
SANTLR: Speech Annotation Toolkit for Low Resource Languages
Xinjian Li, Zhong Zhou, Siddharth Dalmia +2
While low resource speech recognition has attracted a lot of attention from the speech community, there are a few tools available to facilitate low resource speech collection. In t…
Multilingual Speech Recognition with Corpus Relatedness Sampling
Xinjian Li, Siddharth Dalmia, Alan W. Black +1
Multilingual acoustic models have been successfully applied to low-resource speech recognition. Most existing works have combined many small corpora together and pretrained a multi…
Analyzing Utility of Visual Context in Multimodal Speech Recognition Under Noisy Conditions
Tejas Srinivasan, Ramon Sanabria, Florian Metze
Multimodal learning allows us to leverage information from multiple sources (visual, acoustic and text), similar to our experience of the real world. However, it is currently uncle…
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
Suyoun Kim, Siddharth Dalmia, Florian Metze
We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike…
Multimodal Abstractive Summarization for How2 Videos
Shruti Palaskar, Jindrich Libovický, Spandana Gella +1
In this paper, we study abstractive summarization for open-domain videos. Unlike the traditional text news summarization, the goal is less to "compress" text information but rather…
Acoustic-to-Word Models with Conversational Context Information
Suyoun Kim, Florian Metze
Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are t…
The ARIEL-CMU Systems for LoReHLT18
Aditi Chaudhary, Siddharth Dalmia, Junjie Hu +27
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery a…
Phoneme Level Language Models for Sequence Based Low Resource ASR
Siddharth Dalmia, Xinjian Li, Alan W Black +1
Building multilingual and crosslingual models help bring different languages together in a language universal space. It allows models to share parameters and transfer knowledge acr…
Learned In Speech Recognition: Contextual Acoustic Word Embeddings
Shruti Palaskar, Vikas Raunak, Florian Metze
End-to-end acoustic-to-word speech recognition models have recently gained popularity because they are easy to train, scale well to large amounts of training data, and do not requi…