Yulia Tsvetkov

Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning

2016

We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features. The curricula are modeled by a linear ranking function which is …

DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues

2021 · arXiv (Cornell University)

To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential. While modern dialogue agents excel at generating fluent sentences, they still lack pragmatic grounding and …

Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers

2024 · arXiv (Cornell University)

Identifying linguistic differences between dialects of a language often requires expert knowledge and meticulous human analysis. This is largely due to the complexity and nuance involved in studying various dialects. We present a novel approach …

Massively Multilingual Word Embeddings

2016 · arXiv (Cornell University)

We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do …

Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning

2016

Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample, Patrick Littell, David Mortensen, Alan W Black, Lori Levin, Chris Dyer. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: …

Style Transfer Through Back-Translation

2018

Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first …

Measuring Bias in Contextualized Word Representations

2019

Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks. Since they are optimized to capture the statistical properties of training data, they tend to pick up on …