Papers by Roland Vollgraf
Contextual String Embeddings for Sequence Labeling (C18-1)
Copied to clipboard
| Challenge: | Recent advances in language modeling have made it viable to model language as distributions over characters. |
| Approach: | They propose to leverage internal states of a trained character language model to produce a new type of word embeddings. |
| Outcome: | The proposed embeddings outperform the state-of-the-art on four classic sequence labeling tasks. |
ZAP: An Open-Source Multilingual Annotation Projection Framework (L18-1)
Copied to clipboard
| Challenge: | Existing frameworks for annotation projection in parallel corpora limit reproducibility and comparison of experiments. |
| Approach: | They propose an open-source framework for annotation projection in parallel corpora . framework is Java-based and includes methods for preprocessing corpors, computations and visualization . |
| Outcome: | The proposed framework is designed for ease-of-use with lightweight APIs. |
Pooled Contextualized Embeddings for Named Entity Recognition (N19-1)
Copied to clipboard
| Challenge: | Contextual string embeddings are a recent type of word embeddable that are useful for sequence labeling tasks. |
| Approach: | They propose a method that dynamically aggregates contextualized embeddings of each unique string . they then use a pooling operation to distill a ”global” word representation from all contextualized instances . |
| Outcome: | The proposed method improves state-of-the-art for named entity recognition tasks. |
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP (N19-4)
Copied to clipboard
| Challenge: | Existing approaches combine word embeddings with character-level features to model additional features such as subword structures and meaning ambiguity. |
| Approach: | They present FLAIR, an NLP framework that enables embeddings of word and document data . they propose a hierarchical learning architecture that concatenates output states of a character-level CNN or RNN with the output states from a task data. |
| Outcome: | The proposed framework hides embedding-specific engineering complexity and allows researchers to "mix and match" various embeddables with little effort. |
KG-FLIP: Knowledge-guided Fashion-domain Language-Image Pre-training for E-commerce (2023.acl-industry)
Copied to clipboard
| Challenge: | Various visionlanguage pre-training (VLP) models learn cross-modal alignment from large-scale well-aligned image-text datasets without leveraging external knowledge. |
| Approach: | They propose a knowledge-guided fashion-domain language-image pre-training framework that learns fine-grained representations in e-commerce domain and utilizes external knowledge to improve the pre-train efficiency. |
| Outcome: | The proposed framework outperforms state-of-the-art models on Amazon and Fashion-Gen datasets by large margins. |
Task-Aware Representation of Sentences for Generic Text Classification (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to text classification use a transformer architecture with a linear layer on top. |
| Approach: | They propose a transformer-based approach that outputs a class distribution for a given prediction problem. |
| Outcome: | The proposed model outperforms existing approaches on small training data and can learn to predict new classes even with no training examples. |
FEIDEGGER: A Multi-modal Corpus of Fashion Images and Descriptions in German (L18-1)
Copied to clipboard
| Challenge: | Recent years have seen a renewed interest in text-image multi-modality . paired text-picture datasets are often limited to English language text . |
| Approach: | They propose a multi-modal corpus that pairs images and textual descriptions of their content in German to enable study of these challenges. |
| Outcome: | The proposed dataset focuses on the domain of fashion items and their visual descriptions in German. |