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Found 16 papers, 1 papers with code
mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinations
Multilingual sequence-to-sequence models perform poorly with increased language coverage and fail to consistently generate text in the correct target language in few-shot settings.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
We evaluate commonly used models on the benchmark.
Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing
Token free approaches have been successfully applied to a series of word and span level tasks.
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data
While multilingual pretrained language models (LMs) fine-tuned on a single language have shown substantial cross-lingual task transfer capabilities, there is still a wide performance gap in semantic parsing tasks when target language supervision is available.
Answering Conversational Questions on Structured Data without Logical Forms
We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation.
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection
State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention.
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information
In this paper, we combine them by modeling context word similarity in semantic TKs.
