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 arguments (e.g., FrameNet and PropBank), all the recent work on syntaxaware SRL relies on dependency representations of syntax.In contrast, we show how graph convolutional networks (GCNs) can be used to encode constituent structures and inform an SRL system.Nodes in our SpanGCN correspond to constituents.The computation is done in 3 stages.First, initial node representations are produced by 'composing' word representations of the first and last words in the constituent.Second, graph convolutions relying on the constituent tree are performed, yielding syntacticallyinformed constituent representations.Finally, the constituent representations are 'decomposed' back into word representations, which are used as input to the SRL classifier.We evaluate SpanGCN against alternatives, including a model using GCNs over dependency trees, and show its effectiveness on standard English SRL benchmarks CoNLL-2005, CoNLL-2012, and FrameNet.

Marco Del Tredici, Diego Marcheggiani, Sabine Schulte im Walde, Raquel Fernández. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

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 encoder based on graph convolutional networks that directly exploits the input structure.We report results on two graphto-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.1

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 performance on English, even without any kind of syntactic information and only using local inference.However, when automatically predicted partof-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset.We also consider Chinese, Czech and Spanish where our approach also achieves competitive results.Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting.Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.

We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoderdecoder models for machine translation.We rely on graph-convolutional networks (GCNs), a recent class of neural networks developed for modeling graph-structured data.Our GCNs use predicted syntactic dependency trees of source sentences to produce representations of words (i.e.hidden states of the encoder) that are sensitive to their syntactic neighborhoods.GCNs take word representations as input and produce word representations as output, so they can easily be incorporated as layers into standard encoders (e.g., on top of bidirectional RNNs or convolutional neural networks).We evaluate their effectiveness with English-German and English-Czech translation experiments for different types of encoders and observe substantial improvements over their syntax-agnostic versions in all the considered setups.

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, we exploit syntactic information in our model.We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs.GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence.We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-theart LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.

We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.

Salud M. Jiménez Zafra, Giacomo Berardi, Andrea Esuli, Diego Marcheggiani, María Teresa Martín-Valdivia, Alejandro Moreo Fernández. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.

Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most benefit in the creation of a classifier.AL allows to learn accurate systems with much less annotated data than what is required by pure supervised learning algorithms, hence limiting the tedious effort of annotating a large collection of data.We experimentally investigate the behavior of several AL strategies for sequence labeling tasks (in a partially-labeled scenario) tailored on Partially-Labeled Conditional Random Fields, on four sequence labeling tasks: phrase chunking, part-of-speech tagging, named-entity recognition, and bioentity recognition.