Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Marcheggiani, Diego"'
Sentiment analysis (SA) systems are used in many products and hundreds of languages. Gender and racial biases are well-studied in English SA systems, but understudied in other languages, with few resources for such studies. To remedy this, we build a
Externí odkaz:
http://arxiv.org/abs/2305.11673
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer. To improve generalization, we pretrain o
Externí odkaz:
http://arxiv.org/abs/1910.00856
Autor:
Marcheggiani, Diego, Titov, Ivan
Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. Even though most semantic-role formalisms are built upon constituent syntax and only syntactic constituents can be labeled as argument
Externí odkaz:
http://arxiv.org/abs/1909.09814
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP
Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pay attention to their social connections, but exploit this information
Externí odkaz:
http://arxiv.org/abs/1909.00412
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative enco
Externí odkaz:
http://arxiv.org/abs/1810.09995
Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate
Externí odkaz:
http://arxiv.org/abs/1804.08313
We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation. We rely on graph-convolutional networks (GCNs), a recent class of neural networks developed fo
Externí odkaz:
http://arxiv.org/abs/1704.04675
Autor:
Marcheggiani, Diego, Titov, Ivan
Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones,
Externí odkaz:
http://arxiv.org/abs/1703.04826
We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performa
Externí odkaz:
http://arxiv.org/abs/1701.02593
Publikováno v:
Proceedings of the 30th Annual ACM Symposium on Applied Computing (SAC 2015). pp 1066-1071. Salamanca, ES, 2015
Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applica- tions of real-time filtering of great practical interest. We propose t
Externí odkaz:
http://arxiv.org/abs/1611.03350