Papers by Alexey Romanov
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian (C18-1)
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| Challenge: | RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
| Approach: | They propose to use RuSentiment to annotate social media posts in Russian with a kappa of 0.58 and a set of annotation guidelines that are extensible to other languages. |
| Outcome: | The proposed dataset is the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
Revealing the Dark Secrets of BERT (D19-1)
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| Challenge: | Existing models of BERT-based learning systems are lacking specific mechanisms that contribute to its success. |
| Approach: | They propose to use GLUE tasks to analyze the interpretation of self-attention, which is one of the underlying components of BERT. |
| Outcome: | The proposed model outperforms the regular model on GLUE tasks by disabling attention in certain heads. |
Lessons from Natural Language Inference in the Clinical Domain (D18-1)
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| Challenge: | State of the art models with deep neural networks lack generalization capabilities in specialized domains where training data is limited. |
| Approach: | They propose a dataset annotated by doctors performing a natural language inference task grounded in the medical history of patients. |
| Outcome: | The proposed model outperforms existing models in the clinical domain by incorporating domain knowledge from external data and lexical sources. |
Adversarial Decomposition of Text Representation (N19-1)
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| Challenge: | a new method for adversarial decomposition of text representations is proposed . it is capable of fine-grained controlled change of different aspects of the input sentence . |
| Approach: | They propose a method for adversarial decomposition of text representation . they use vectors responsible for a specific aspect of the input sentence . |
| Outcome: | The proposed method outperforms the embeddings of a regular autoencoder on paraphrase detection tasks. |
Similarity-Based Reconstruction Loss for Meaning Representation (D18-1)
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| Challenge: | a new paper proposes and evaluates a set of loss functions that can be used to train models for representation learning . cross-entropy loss penalizes models when they fail to generate the exact word from ground truth data . |
| Approach: | They propose and evaluate loss functions that can be used to train any neural model for representation learning. |
| Outcome: | The proposed loss functions amplify semantic diversity while preserving original meaning . they show performance improvement on paraphrase detection and language inference tasks . |
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (N19-1)
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Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai
| Challenge: | Existing methods for mitigating bias in machine learning systems rely on access to protected attributes such as race, gender, or age. |
| Approach: | They propose a method for discouraging correlation between predicted probability of an individual’s true occupation and a word embedding of their name. |
| Outcome: | The proposed method reduces race and gender biases, with almost no reduction in the classifier’s overall true positive rate. |