Search Results for author:
Found 25 papers, 14 papers with code
TUDa at WMT21: Sentence-Level Direct Assessment with Adapters
This paper presents our submissions to the WMT2021 Shared Task on Quality Estimation, Task 1 Sentence-Level Direct Assessment.
MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer
While offering (1) improved fine-tuning efficiency (by a factor of around 50 in our experiments), (2) a smaller parameter budget, and (3) increased language coverage, MAD-G remains competitive with more expensive methods for language-specific adapter training across the board.
Lifting the Curse of Multilinguality by Pre-training Modular Transformers
Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages.
UKP-SQUARE: An Online Platform for Question Answering Research
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).
Delving Deeper into Cross-lingual Visual Question Answering
Previous work on cross-lingual VQA has reported poor zero-shot transfer performance of current multilingual multimodal Transformers and large gaps to monolingual performance, attributed mostly to misalignment of text embeddings between the source and target languages, without providing any additional deeper analyses.
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups.
Cross-Lingual Visual Natural Language Inference
Cross-Modal Retrieval
+15
xGQA: Cross-Lingual Visual Question Answering
In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task.
TxT: Crossmodal End-to-End Learning with Transformers
Reasoning over multiple modalities, e. g. in Visual Question Answering (VQA), requires an alignment of semantic concepts across domains.
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR.
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters
The open-access dissemination of pretrained language models through online repositories has led to a democratization of state-of-the-art natural language processing (NLP) research.
What to Pre-Train on? Efficient Intermediate Task Selection
Our best methods achieve an average Regret@3 of less than 1% across all target tasks, demonstrating that we are able to efficiently identify the best datasets for intermediate training.
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining.
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance.
AdapterDrop: On the Efficiency of Adapters in Transformers
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements.
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines.
AdapterHub: A Framework for Adapting Transformers
We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages.
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
We show that by separating the two stages, i. e., knowledge extraction and knowledge composition, the classifier can effectively exploit the representations learned from multiple tasks in a non-destructive manner.
Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields
We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks.
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer.
Ranked #2 on
Cross-Lingual Transfer
on XCOPA
(using extra training data)
Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on FineGrained Propaganda Detection.
What do Deep Networks Like to Read?
Recent research towards understanding neural networks probes models in a top-down manner, but is only able to identify model tendencies that are known a priori.
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning
Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data.
Specializing Distributional Vectors of All Words for Lexical Entailment
Semantic specialization methods fine-tune distributional word vectors using lexical knowledge from external resources (e. g. WordNet) to accentuate a particular relation between words.
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval
In this study, we investigate learning-to-rank and query refinement approaches for information retrieval in the pharmacogenomic domain.
