Papers by Olga Vechtomova
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction (2020.acl-main)
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| Challenge: | Sentence summarization systems that use latent space to reconstruct the source sentence are unwillingly exploited. |
| Approach: | They propose a method that uses language modeling and semantic similarity metrics to find a high-scoring summary. |
| Outcome: | The proposed method achieves state-of-the-art for unsupervised sentence summarization according to ROUGE scores. |
Adversarial Learning on the Latent Space for Diverse Dialog Generation (2020.coling-main)
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| Challenge: | Existing methods for dialog generation generate generic utterances, e.g., always generating "I don't know" |
| Approach: | They propose a framework that uses generative adversarial nets to generate conditioned responses in dialogs. |
| Outcome: | The proposed model generates more fluent, relevant, and diverse responses than state-of-the-art methods. |
Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
| Approach: | They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence. |
| Outcome: | The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models. |
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation (2023.emnlp-main)
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| Challenge: | Recent work often tackles the problem of text classification when there is a limited amount of training data. |
| Approach: | They propose a method to generate more helpful augmented data by utilizing the LLM's ability to follow instructions and perform few-shot classifications. |
| Outcome: | The proposed method generates more helpful examples near class boundaries, but generating borderline examples increases the risk of false positives in the dataset. |
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation (2021.eacl-main)
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| Challenge: | Existing methods for learning disentangled representations of real-world data focus on attribute labels or unsupervised methods that manipulate factorization in the latent space of models such as the variational autoencoder (VAE). |
| Approach: | They propose an approach that disentangles select attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes. |
| Outcome: | The proposed method outperforms the VAE baseline and is competitive with state-of-the-art approaches while being more a general framework applicable to other attribute disentanglement tasks. |
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches (2025.findings-naacl)
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| Challenge: | Existing approaches for low-resource text summarization use large language models (LLMs) but such models suffer from inconsistent outputs and are difficult to adapt to domain-specific data. |
| Approach: | They propose two methods to effectively utilize large language models for low-resource text summarization. |
| Outcome: | The proposed methods synthesize high-quality documents using LLaMA-3-70b-Instruct model . they achieve competitive ROUGE scores as a fully supervised method with 5% of the labeled data. |
Disentangled Representation Learning for Non-Parallel Text Style Transfer (P19-1)
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| Challenge: | a paper aims to disentangle latent representations of style and content in language models . auxiliary multi-task and adversarial objectives are used to disentangle the latent space . |
| Approach: | They propose a simple yet effective approach to disentangling latent representations . they propose auxiliary multi-task and adversarial objectives to disentangle style and content . |
| Outcome: | The proposed approach achieves high performance in terms of transfer accuracy, content preservation, and language fluency compared to previous approaches . |
Iterative Edit-Based Unsupervised Sentence Simplification (2020.acl-main)
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| Challenge: | Sentence simplification is relevant in various real-world and downstream applications. |
| Approach: | They propose an edit-based approach to unsupervised sentence simplification that uses a scoring function to score fluency, simplicity, and meaning preservation to perform edits. |
| Outcome: | The proposed model is more controllable and interpretable than state-of-the-art models on newsela and WikiLarge datasets. |
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (N19-1)
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| Challenge: | Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs. |
| Approach: | They propose to use the variational autoencoder (VAE) for probabilistic sentence generation . they propose a variant of WAE that encourages the stochasticity of the encoder . |
| Outcome: | The proposed variant encourages the stochasticity of the encoder while achieving higher BLEU scores. |
Variational Attention for Sequence-to-Sequence Models (C18-1)
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| Challenge: | Existing variational autoencoders encode data to latent variables and then decode them into target data. |
| Approach: | They propose a variational attention mechanism where the attention vector is also modeled as Gaussian distributed random variables. |
| Outcome: | The proposed method reduces the variational latent space bypassing phenomenon as it increases diversity of generated sentences. |
Adaptive Fusion Techniques for Multimodal Data (2021.eacl-main)
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| Challenge: | Effective fusion of data from multiple modalities is challenging due to the heterogeneous nature of multimodal data. |
| Approach: | They propose two adaptive fusion techniques that aim to combine multimodal data effectively. |
| Outcome: | The proposed networks can model context from other modalities better than existing methods. |
Stylized Text Generation: Approaches and Applications (2020.acl-tutorials)
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| Challenge: | Text generation has played an important role in various applications of natural language processing. |
| Approach: | They present different settings of stylized text generation and introduce machine learning methods to represent style. |
| Outcome: | This paper presents a comprehensive literature review on stylized text generation . it focuses on the challenges and future directions of stylized generation based on machine learning . |