Papers by Victor Prokhorov
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models (D18-1)
Copied to clipboard
| Challenge: | Existing benchmarks for rare word representation are lacking for evaluation and comparison . a task-based evaluation does not provide a solid basis for comparing different models . |
| Approach: | They propose to use an expert-annotated word similarity dataset to evaluate rare word representation techniques. |
| Outcome: | The proposed dataset provides a reliable benchmark for rare word representation techniques. |
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation (D19-56)
Copied to clipboard
| Challenge: | Variational Autoencoders suffer from learning uninformative latent representations due to issues such as approximated posterior collapse or entanglement of the latent space. |
| Approach: | They propose to impose an explicit constraint on the Kullback-Leibler divergence term inside the VAE objective function to understand the significance of the KL term in controlling the information transmitted through the VAe channel. |
| Outcome: | The proposed constraint avoids posterior collapse, but it also controls the information transmitted through the VAE channel. |
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models (N19-1)
Copied to clipboard
| Challenge: | a novel method for mapping unrestricted text to knowledge graph entities is proposed . a proof-of-concept experiment has encouraging results comparable to those of state-of the-art systems. |
| Approach: | They propose a method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. |
| Outcome: | The proposed method produces highly interpretable predictions comparable to state-of-the-art systems. |
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure (2023.emnlp-main)
Copied to clipboard
| Challenge: | Structured Autoencoder framework StrAE enables effective learning of multi-level representations through strict adherence to explicit structure. |
| Approach: | They propose a Structured Autoencoder framework that strictly adheres to explicit structure and uses a contrastive objective over tree-structured representations. |
| Outcome: | The proposed framework outperforms baselines that don’t involve explicit hierarchical compositions and is comparable to models given informative structure. |