360 research outputs found
When silver glitters more than gold: Bootstrapping an Italian part-of-speech tagger for Twitter
We bootstrap a state-of-the-art part-of-speech tagger to tag Italian Twitter
data, in the context of the Evalita 2016 PoSTWITA shared task. We show that
training the tagger on native Twitter data enriched with little amounts of
specifically selected gold data and additional silver-labelled data scraped
from Facebook, yields better results than using large amounts of manually
annotated data from a mix of genres.Comment: Proceedings of the 5th Evaluation Campaign of Natural Language
Processing and Speech Tools for Italian (EVALITA 2016
The Impact of Annotation on the Performance of Protein Tagging in Biomedical Text
In this paper we discuss five different corpora annotated for protein names. We present several within- and cross-dataset protein tagging experiments showing that different annotation schemes severely affect the portability of statistical protein taggers. By means of a detailed error analysis we identify crucial annotation issues that future annotation projects should take into careful consideration
IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation
The T5 model and its unified text-to-text paradigm contributed in advancing
the state-of-the-art for many natural language processing tasks. While some
multilingual variants of the T5 model have recently been introduced, their
performances were found to provide suboptimal performances for languages other
than English if compared to monolingual variants. We are motivated by these
findings to introduce IT5, the first family of encoder-decoder transformer
models pretrained specifically on Italian. We perform a thorough cleaning of a
web-crawled Italian corpus including more than 40 billion words and use it to
pretrain three IT5 models of different sizes. The performance of IT5 models and
their multilingual counterparts is then evaluated on a broad range of natural
language understanding and generation benchmarks for Italian. We find the
monolingual IT5 models to provide the best scale-to-performance ratio across
tested models, consistently outperforming their multilingual counterparts and
setting a new state-of-the-art for most Italian conditional language generation
tasks.Comment: 13 pages, 7 tables, 1 figure. Code and checkpoints available:
https://github.com/gsarti/it
Syntactic Features and Word Similarity for Supervised Metonymy Resolution
We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparseness
Multi-Figurative Language Generation
Figurative language generation is the task of reformulating a given text in the desired figure of speech while still being faithful to the original context. We take the first step towards multi-figurative language modelling by providing a benchmark for the automatic generation of five common figurative forms in English. We train mFLAG employing a scheme for multi-figurative language pre-training on top of BART, and a mechanism for injecting the target figurative information into the encoder; this enables the generation of text with the target figurative form from another figurative form without parallel figurative-figurative sentence pairs. Our approach outperforms all strong baselines. We also offer some qualitative analysis and reflections on the relationship between the different figures of speech
Breeding Fillmoreās Chickens and Hatching the Eggs:Recombining Frames and Roles in Frame-Semantic Parsing
Frame-semantic parsers traditionally predict predicates, frames, and semantic roles in a fixed order. This paper explores the āchicken-or-eggā problem of interdependencies between these components theoretically and practically. We introduce a flexible BERT-based sequence labeling architecture that allows for predicting frames and roles independently from each other or combining them in several ways. Our results show that our setups can approximate more complex traditional modelsā performance, while allowing for a clearer view of the interdependencies between the pipelineās components, and of how frame and role prediction models make different use of BERTās layers
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