A context based model for sentiment analysis in twitter for the italian language

Studi recenti per la Sentiment Analysis in Twitter hanno tentato di creare modelli per caratterizzare la polarit´a di un tweet osservando ciascun messaggio in isolamento. In realt`a, i tweet fanno parte di conversazioni, la cui natura pu`o essere sfruttata per migliorare la qualit`a dell’analisi da parte di sistemi automatici. In (Vanzo et al., 2014) `e stato proposto un modello basato sulla classificazione di sequenze per la caratterizzazione della polarit` a dei tweet, che sfrutta il contesto in cui il messaggio `e immerso. In questo lavoro, si vuole verificare l’applicabilit`a di tale metodologia anche per la lingua Italiana.Recent works on Sentiment Analysis over Twitter leverage the idea that the sentiment depends on a single incoming tweet. However, tweets are plunged into streams of posts, thus making available a wider context. The contribution of this information has been recently investigated for the English language by modeling the polarity detection as a sequential classification task over streams of tweets (Vanzo et al., 2014). Here, we want to verify the applicability of this method even for a morphological richer language, i.e. Italian

A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction

Robust Spoken Language Understanding for House Service Robots

Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS

Deep Learning for Automatic Image Captioning in Poor Training Conditions

Recent advancements in Deep Learning have proved that an architecture that combines Convolutional Neural Networks and Recurrent Neural Networks enables the definition of very effective methods for the automatic captioning of images. The disadvantage that comes with this straightforward result is that this approach requires the existence of large-scale corpora, which are not available for many languages.This paper introduces a simple methodology to automatically acquire a large-scale corpus of 600 thousand image/sentences pairs in Italian. At the best of our knowledge, this corpus has been used to train one of the first neural captioning systems for the same language. The experimental evaluation over a subset of validated image/captions pairs suggests that the achieved results are comparable with the English counterpart, despite a reduced amount of training examples

GAN-BERT: Generative adversarial learning for robust text classification with a bunch of labeled examples

Recent Transformer-based architectures, e.g., BERT, provide impressive results in many Natural Language Processing tasks. However, most of the adopted benchmarks are made of (sometimes hundreds of) thousands of examples. In many real scenarios, obtaining high- quality annotated data is expensive and time consuming; in contrast, unlabeled examples characterizing the target task can be, in general, easily collected. One promising method to enable semi-supervised learning has been proposed in image processing, based on Semi- Supervised Generative Adversarial Networks. In this paper, we propose GAN-BERT that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. Experimental results show that the requirement for annotated examples can be drastically reduced (up to only 50-100 annotated examples), still obtaining good performances in several sentence classification tasks

On the Readability of Deep Learning Models: the role of Kernel-based Deep Architectures

Deep Neural Networks achieve state-of-the-art performances in several semantic NLP tasks but lack of explanation capabilities as for the limited interpretability of the underlying acquired models. In other words, tracing back causal connections between the linguistic properties of an input instance and the produced classification is not possible. In this paper, we propose to apply Layerwise Relevance Propagation over linguistically motivated neural architectures, namely Kernel-based Deep Architectures (KDA), to guide argumentations and explanation inferences. In this way, decisions provided by a KDA can be linked to the semantics of input examples, used to linguistically motivate the network output.Le Deep Neural Network raggiungono oggi lo stato dell’arte in molti processi di NLP, ma la scarsa interpretabilitá dei modelli risultanti dall’addestramento limita la comprensione delle loro inferenze. Non é possibile cioé determinare connessioni causali tra le proprietá linguistiche di un esempio e la classificazione prodotta dalla rete. In questo lavoro, l’applicazione della Layerwise Relevance Propagation alle Kernel-based Deep Architecture (KDA) é usata per determinare connessioni tra la semantica dell’input e la classe di output che corrispondono a spiegazioni linguistiche e trasparenti della decisione

Nystrom methods for efficient kernel-based methods for community question answering

Expressive but complex kernel functions, such as Sequence or Tree kernels, are usually underemployed in NLP tasks, e.g., in community Question Answering (cQA), as for their significant complexity in both learning and classification stages. Recently, the Nyström methodology for data embedding has been proposed as a viable solution to scalability problems. By mapping data into low-dimensional approximations of kernel spaces, it positively increases scalability through compact linear representations for highly structured data. In this paper, we show that Nyström methodology can be effectively used to apply a kernel-based method in the cQA task, achieving state-of-the-art results by reducing the computational cost of orders of magnitude. Metodi di apprendimento automatico basato su funzioni kernel complesse, come Sequence o Tree Kernel, rischiano di non poter essere adeguatamente utilizzati in problemi legati all'elaborazione del linguaggio naturale (come ad esempio in Community Question Answering) a causa degli alti costi computazionali per l'addestramento e la classificazione. Recentemente è stata proposta una metodologia, basata sul metodo di Nyström, per poter far fronte a questi problemi di scalabilità: essa permette di proiettare gli esempi, osservabili in fase di addestramento e classificazione, all'interno di spazi a bassa dimensionalità che approssimano lo spazio sottostante la funzione kernel. Queste rappresentazioni compatte permettono di applicare algoritmi di apprendimento automatico estremamente efficienti e scalabili. In questo lavoro si dimostra che è possibile applicare metodi kernel al problema di Community Question Answering, ottenendo risultati che sono lo stato dell'arte, riducendo di ordini di grandezza i costi computazionali

explaining non linear classifier decisions within kernel based deep architectures

n/

On the Readability of Kernel-based Deep Learning Models in Semantic Role Labeling Tasks over Multiple Languages

Sentence embeddings are effective input vectors for the neural learning of a number of inferences about content and meaning. Unfortunately, most of such decision processes are epistemologically opaque as for the limited interpretability of the acquired neural models based on the involved embeddings. In this paper, we concentrate on the readability of neural models, discussing an embedding technique (the Nyström methodology) that corresponds to the reconstruction of a sentence in a kernel space, capturing grammatical and lexical semantic information. From this method, we build a Kernel-based Deep Architecture that is characterized by inherently high interpretability properties, as the proposed embedding is derived from examples, i.e., landmarks, that are both human readable and labeled. Its integration with an explanation methodology, the Layer-wise Relevance Propagation, supports here the automatic compilation of argumentations for the Kernel-based Deep Architecture decisions, expressed in form of analogy with activated landmarks. Quantitative evaluation against the Semantic Role Labeling task, both in English and Italian, suggests that explanations based on semantic and syntagmatic structures are rich and characterize convincing arguments, as they effectively help the user in assessing whether or not to trust the machine decisions