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Found 11 papers, 4 papers with code
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
Document translation poses a challenge for Neural Machine Translation (NMT) systems.
A Multi-Modal Multilingual Benchmark for Document Image Classification
Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents.
Diable: Efficient Dialogue State Tracking as Operations on Tables
Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn.
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability
(2) Does the answer to that question change with model adaptation?
Semi-Supervised Joint Estimation of Word and Document Readability
Readability or difficulty estimation of words and documents has been investigated independently in the literature, often assuming the existence of extensive annotated resources for the other.
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI).
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity
Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space.
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification
Text classification must sometimes be applied in a low-resource language with no labeled training data.
Cross-Lingual Document Classification
Document Classification
+3
Substring Frequency Features for Segmentation of Japanese Katakana Words with Unlabeled Corpora
Furthermore, it improves performance of simple word segmentation models trained on a manually labeled corpus.
