activity
20162022
most citedA Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation

31 citations · 96 across the 14 of their papers we have counts for

collaborators

35 papers

cs.IR2022

Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models

Suraj Nair, Eugene Yang, Dawn Lawrie +5

The advent of transformer-based models such as BERT has led to the rise of neural ranking models. These models have improved the effectiveness of retrieval systems well beyond that…

cs.CL2021

An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces

Kelly Marchisio, Youngser Park, Ali Saad-Eldin +4

Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that…

eess.AS20214 cited

Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring

Hirofumi Inaguma, Yosuke Higuchi, Kevin Duh +2

This article describes an efficient end-to-end speech translation (E2E-ST) framework based on non-autoregressive (NAR) models. End-to-end speech translation models have several adv…

eess.AS20211 cited

ESPnet-ST IWSLT 2021 Offline Speech Translation System

Hirofumi Inaguma, Brian Yan, Siddharth Dalmia +4

This paper describes the ESPnet-ST group's IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audi…

cs.CL2021

Self-Guided Curriculum Learning for Neural Machine Translation

Lei Zhou, Liang Ding, Kevin Duh +3

In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i.e. the synthetic labels predicted by the model should be as close t…

eess.AS20217 cited

Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yoloxóchitl Mixtec

Jiatong Shi, Jonathan D. Amith, Rey Castillo García +3

"Transcription bottlenecks", created by a shortage of effective human transcribers are one of the main challenges to endangered language (EL) documentation. Automatic speech recogn…