49 citations · 80 across the 14 of their papers we have counts for
6 papers · 1 filter
A Universal Parent Model for Low-Resource Neural Machine Translation Transfer
Mozhdeh Gheini, Jonathan May
Transfer learning from a high-resource language pair `parent' has been proven to be an effective way to improve neural machine translation quality for low-resource language pairs `…
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis
Xiaolei Huang, Jonathan May, Nanyun Peng
Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingu…
Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games
Xusen Yin, Jonathan May
We consider the task of learning to play families of text-based computer adventure games, i.e., fully textual environments with a common theme (e.g. cooking) and goal (e.g. prepare…
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation
Nima Pourdamghani, Nada Aldarrab, Marjan Ghazvininejad +2
Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore…
Comprehensible Context-driven Text Game Playing
Xusen Yin, Jonathan May
In order to train a computer agent to play a text-based computer game, we must represent each hidden state of the game. A Long Short-Term Memory (LSTM) model running over observed…
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages
Ronald Cardenas, Ying Lin, Heng Ji +1
Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to \textit{ground} their clusters as well. Grounding generally require…