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Found 13 papers, 6 papers with code
SALTED: A Framework for SAlient Long-Tail Translation Error Detection
Traditional machine translation (MT) metrics provide an average measure of translation quality that is insensitive to the long tail of behavioral problems in MT.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks
In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks.
The Curious Case of Hallucinations in Neural Machine Translation
In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies.
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.
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Data-to-Text Generation
on WebNLG ru
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
On Long-Tailed Phenomena in Neural Machine Translation
State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens, tackling which remains a major challenge.
Ranking Clarification Questions via Natural Language Inference
Given a natural language query, teaching machines to ask clarifying questions is of immense utility in practical natural language processing systems.
On Compositionality in Neural Machine Translation
We investigate two specific manifestations of compositionality in Neural Machine Translation (NMT) : (1) Productivity - the ability of the model to extend its predictions beyond the observed length in training data and (2) Systematicity - the ability of the model to systematically recombine known parts and rules.
On Leveraging the Visual Modality for Neural Machine Translation
Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics.
On Dimensional Linguistic Properties of the Word Embedding Space
Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties.
Effective Dimensionality Reduction for Word Embeddings
Pre-trained word embeddings are used in several downstream applications as well as for constructing representations for sentences, paragraphs and documents.
Learned In Speech Recognition: Contextual Acoustic Word Embeddings
End-to-end acoustic-to-word speech recognition models have recently gained popularity because they are easy to train, scale well to large amounts of training data, and do not require a lexicon.
Simple and Effective Dimensionality Reduction for Word Embeddings
Word embeddings have become the basic building blocks for several natural language processing and information retrieval tasks.
