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Found 11 papers, 4 papers with code
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing
In addition, we show the applicability of our approach in a completely unsupervised setting.
Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
To this end, we propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve the semantic coverage.
EaSe: A Diagnostic Tool for VQA based on Answer Diversity
We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems
Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling.
Can Pre-training help VQA with Lexical Variations?
However, these models fail to perform well on rephrasings of a question, which raises some important questions like Are these models robust towards linguistic variations?
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal.
P $\approx$ NP, at least in Visual Question Answering
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets.
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA
We show that our metric is effective in providing a more fine-grained evaluation both on the quantitative and qualitative level.
How do Convolutional Neural Networks Learn Design?
We use LRP to explain the pixel-wise contributions of book cover design and highlight the design elements contributing towards particular genres.
